subject:(Nonnegative Matrix Factorization)

Northeastern University

1. Wang, Yueyang. New initialization strategy for nonnegative matrix factorization.

Degree: MS, Department of Electrical and Computer Engineering, 2018, Northeastern University

URL: http://hdl.handle.net/2047/D20290549

► Nonnegative matrix factorization (NMF) has been proved to be a powerful data representa-tion method, and has shown success in applications such as data representation and… (more)

Subjects/Keywords: complementary elements; nonnegative matrix factorization; data

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APA (6^th Edition):

Wang, Y. (2018). New initialization strategy for nonnegative matrix factorization. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20290549

Chicago Manual of Style (16^th Edition):

Wang, Yueyang. “New initialization strategy for nonnegative matrix factorization.” 2018. Masters Thesis, Northeastern University. Accessed March 06, 2021. http://hdl.handle.net/2047/D20290549.

MLA Handbook (7^th Edition):

Wang, Yueyang. “New initialization strategy for nonnegative matrix factorization.” 2018. Web. 06 Mar 2021.

Vancouver:

Wang Y. New initialization strategy for nonnegative matrix factorization. [Internet] [Masters thesis]. Northeastern University; 2018. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/2047/D20290549.

Council of Science Editors:

Wang Y. New initialization strategy for nonnegative matrix factorization. [Masters Thesis]. Northeastern University; 2018. Available from: http://hdl.handle.net/2047/D20290549

University of Minnesota

2. Mosesov, Artem. Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization.

Degree: M.S.E.E., Electrical Engineering, 2014, University of Minnesota

URL: http://hdl.handle.net/11299/173949

► Problems with non-negativity constrains have recently attracted a great deal of interest. Non-negativity constraints arise naturally in many applications, and are often necessary for proper… (more)

Subjects/Keywords: least squares; matrix factorization; NMF; NNLS; Nonnegative

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APA (6^th Edition):

Mosesov, A. (2014). Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/173949

Chicago Manual of Style (16^th Edition):

Mosesov, Artem. “Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization.” 2014. Masters Thesis, University of Minnesota. Accessed March 06, 2021. http://hdl.handle.net/11299/173949.

MLA Handbook (7^th Edition):

Mosesov, Artem. “Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization.” 2014. Web. 06 Mar 2021.

Vancouver:

Mosesov A. Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization. [Internet] [Masters thesis]. University of Minnesota; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/11299/173949.

Council of Science Editors:

Mosesov A. Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization. [Masters Thesis]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/173949

University of Western Ontario

3. Feng, Boyu. Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.

Degree: 2018, University of Western Ontario

URL: https://ir.lib.uwo.ca/etd/5732

► The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on… (more)

▼ The emergence of hyperspectral imaging provides a new perspective for Earth observation, in addition to previously available orthophoto and multispectral imagery. This thesis focused on both the new data and new methodology in the field of hyperspectral imaging. First, the application of the future hyperspectral satellite EnMAP in impervious surface area (ISA) mapping was studied. During the search for the appropriate ISA mapping procedure for the new data, the subpixel classification based on nonnegative matrix factorization (NMF) achieved the best success. The simulated EnMAP image shows great potential in urban ISA mapping with over 85% accuracy. Unfortunately, the NMF based on the linear algebra only considers the spectral information and neglects the spatial information in the original image. The recent wide interest of applying the multilinear algebra in computer vision sheds light on this problem and raised the idea of nonnegative tensor factorization (NTF). This thesis found that the NTF has more advantages over the NMF when work with medium- rather than the high-spatial-resolution hyperspectral image. Furthermore, this thesis proposed to equip the NTF-based subpixel classification methods with the variations adopted from the NMF. By adopting the variations from the NMF, the urban ISA mapping results from the NTF were improved by ~2%. Lastly, the problem known as the curse of dimensionality is an obstacle in hyperspectral image applications. The majority of current dimension reduction (DR) methods are restricted to using only the spectral information, when the spatial information is neglected. To overcome this defect, two spectral-spatial methods: patch-based and tensor-patch-based, were thoroughly studied and compared in this thesis. To date, the popularity of the two solutions remains in computer vision studies and their applications in hyperspectral DR are limited. The patch-based and tensor-patch-based variations greatly improved the quality of dimension-reduced hyperspectral images, which then improved the land cover mapping results from them. In addition, this thesis proposed to use an improved method to produce an important intermediate result in the patch-based and tensor-patch-based DR process, which further improved the land cover mapping results.

Subjects/Keywords: Nonnegative matrix factorization; nonnegative tensor factorization; hyperspectral image; spectral unmixing; dimension reduction; Geographic Information Sciences; Remote Sensing

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APA (6^th Edition):

Feng, B. (2018). Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/5732

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Feng, Boyu. “Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.” 2018. Thesis, University of Western Ontario. Accessed March 06, 2021. https://ir.lib.uwo.ca/etd/5732.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Feng, Boyu. “Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.” 2018. Web. 06 Mar 2021.

Vancouver:

Feng B. Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping. [Internet] [Thesis]. University of Western Ontario; 2018. [cited 2021 Mar 06]. Available from: https://ir.lib.uwo.ca/etd/5732.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Feng B. Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping. [Thesis]. University of Western Ontario; 2018. Available from: https://ir.lib.uwo.ca/etd/5732

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

4. Ling, Chenyang (author). Nonnegative Robust PCA for Background and Foreground Image Decomposition.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:08125ab1-9db3-4ce8-9fa9-cea5ed7cc257

►

Nowadays, video surveillance and motion detection system are widely used in various environments. With the relatively low-price cameras and highly automated monitoring system, video and… (more)

▼

Nowadays, video surveillance and motion detection system are widely used in various environments. With the relatively low-price cameras and highly automated monitoring system, video and image analysis on road, highway and skies becomes realistic. The key process in the analysis is to separate the useful information such as moving foreground objects from the original video sequence where Robust Principal Component Analysis (RPCA) plays an important role in extracting the foreground objects. RPCA have been widely used in data analysis and dimension reduction with applications in image recovery, information clustering and computer vision. But one drawback of RPCA lies in the fact that it does not guarantee the nonnegativity of pixels. It is important to have nonnegative foreground object since negative pixels that are not in the range between 0 and 255 are meaningless and the foreground objects are thus not visible. State-of-the-art methods do not consider the nonnegativity of the foreground object in their algorithms. This thesis focuses on the problem of extracting foreground moving object from background scenes and guarantee the nonnegativity of foreground object. This thesis proposes a method that combines RPCA and Nonnegative Matrix Factorization (NMF). It ensures the pixels that constitute the foreground object is nonnegative by using the basic model of RPCA and nonnegative components that NMF provides. The efficacy of the proposed algorithms is tested on publicly available dataset. Experiment shows in detail how the proposed algorithms achieve in recovering the foreground object with high true positive rate. Together with RPCA algorithm, the performance of recovery is compared and their advantages and disadvantages are discussed.

Mechanical Engineering | Systems and Control

Advisors/Committee Members: Batselier, K. (mentor), van Wingerden, J.W. (graduation committee), van de Plas, R. (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Robust Principal Component Analysis; Nonnegative Matrix Factorization; Image decomposition

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APA (6^th Edition):

Ling, C. (. (2020). Nonnegative Robust PCA for Background and Foreground Image Decomposition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:08125ab1-9db3-4ce8-9fa9-cea5ed7cc257

Chicago Manual of Style (16^th Edition):

Ling, Chenyang (author). “Nonnegative Robust PCA for Background and Foreground Image Decomposition.” 2020. Masters Thesis, Delft University of Technology. Accessed March 06, 2021. http://resolver.tudelft.nl/uuid:08125ab1-9db3-4ce8-9fa9-cea5ed7cc257.

MLA Handbook (7^th Edition):

Ling, Chenyang (author). “Nonnegative Robust PCA for Background and Foreground Image Decomposition.” 2020. Web. 06 Mar 2021.

Vancouver:

Ling C(. Nonnegative Robust PCA for Background and Foreground Image Decomposition. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 06]. Available from: http://resolver.tudelft.nl/uuid:08125ab1-9db3-4ce8-9fa9-cea5ed7cc257.

Council of Science Editors:

Ling C(. Nonnegative Robust PCA for Background and Foreground Image Decomposition. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:08125ab1-9db3-4ce8-9fa9-cea5ed7cc257

University of Kentucky

5. Thapa, Nirmal. CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION.

Degree: 2013, University of Kentucky

URL: https://uknowledge.uky.edu/cs_etds/15

► Data are valuable assets to any organizations or individuals. Data are sources of useful information which is a big part of decision making. All sectors… (more)

▼ Data are valuable assets to any organizations or individuals. Data are sources of useful information which is a big part of decision making. All sectors have potential to benefit from having information. Commerce, health, and research are some of the fields that have benefited from data. On the other hand, the availability of the data makes it easy for anyone to exploit the data, which in many cases are private confidential data. It is necessary to preserve the confidentiality of the data. We study two categories of privacy: Data Value Hiding and Data Pattern Hiding. Privacy is a huge concern but equally important is the concern of data utility. Data should avoid privacy breach yet be usable. Although these two objectives are contradictory and achieving both at the same time is challenging, having knowledge of the purpose and the manner in which it will be utilized helps. In this research, we focus on some particular situations for clustering and classification problems and strive to balance the utility and privacy of the data. In the first part of this dissertation, we propose Nonnegative Matrix Factorization (NMF) based techniques that accommodate constraints defined explicitly into the update rules. These constraints determine how the factorization takes place leading to the favorable results. These methods are designed to make alterations on the matrices such that user-specified cluster properties are introduced. These methods can be used to preserve data value as well as data pattern. As NMF and K-means are proven to be equivalent, NMF is an ideal choice for pattern hiding for clustering problems. In addition to the NMF based methods, we propose methods that take into account the data structures and the attribute properties for the classification problems. We separate the work into two different parts: linear classifiers and nonlinear classifiers. We propose two different solutions based on the classifiers. We study the effect of distortion on the utility of data. We propose three distortion measurement metrics which demonstrate better characteristics than the traditional metrics. The effectiveness of the measures is examined on different benchmark datasets. The result shows that the methods have the desirable properties such as invariance to translation, rotation, and scaling.

Subjects/Keywords: Privacy Preserving Data Mining; Nonnegative Matrix Factorization; Constraints on Nonnegative Matrix Factorization; Correlation; Neighborhood; Distortion Metrics; Clustering; Classification; Computer Sciences; Databases and Information Systems; Other Computer Sciences

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APA (6^th Edition):

Thapa, N. (2013). CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/cs_etds/15

Chicago Manual of Style (16^th Edition):

Thapa, Nirmal. “CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION.” 2013. Doctoral Dissertation, University of Kentucky. Accessed March 06, 2021. https://uknowledge.uky.edu/cs_etds/15.

MLA Handbook (7^th Edition):

Thapa, Nirmal. “CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION.” 2013. Web. 06 Mar 2021.

Vancouver:

Thapa N. CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION. [Internet] [Doctoral dissertation]. University of Kentucky; 2013. [cited 2021 Mar 06]. Available from: https://uknowledge.uky.edu/cs_etds/15.

Council of Science Editors:

Thapa N. CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION. [Doctoral Dissertation]. University of Kentucky; 2013. Available from: https://uknowledge.uky.edu/cs_etds/15

Vanderbilt University

6. Zhong, Xue. Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping.

Degree: MS, Biostatistics, 2015, Vanderbilt University

URL: http://hdl.handle.net/1803/13137

► Cancers are complex diseases and identification of clinically important subtypes has the potential to guide better prognosis and treatment. The utility of graph-regularized nonnegative matrix… (more)

Subjects/Keywords: sparse; network-regularized; nonnegative matrix factorization; tumor subtyping; classification; network-based stratification; somatic mutation; TCGA

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APA (6^th Edition):

Zhong, X. (2015). Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13137

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhong, Xue. “Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping.” 2015. Thesis, Vanderbilt University. Accessed March 06, 2021. http://hdl.handle.net/1803/13137.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhong, Xue. “Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping.” 2015. Web. 06 Mar 2021.

Vancouver:

Zhong X. Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping. [Internet] [Thesis]. Vanderbilt University; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1803/13137.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhong X. Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping. [Thesis]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/13137

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

7. Yang, Kai-Jhih. Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification.

Degree: Master, Institute Of Applied Mathematics, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-155316

► There are plenty of medical image data which have three-dimensional tensor structure. While analyzing the image data, due to the high dimensionality of the data,… (more)

Subjects/Keywords: Nonnegative Matrix Factorization; Multilayer Perceptron; Image Recognition; Adaptive Moment Estimation; Cross Validation

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APA (6^th Edition):

Yang, K. (2018). Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-155316

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yang, Kai-Jhih. “Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification.” 2018. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-155316.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yang, Kai-Jhih. “Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification.” 2018. Web. 06 Mar 2021.

Vancouver:

Yang K. Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-155316.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang K. Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-155316

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

8. Charles, Richard Martin. Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images.

Degree: PhD, Applied Mathematics, 2015, University of Colorado

URL: https://scholar.colorado.edu/appm_gradetds/68

► Recent improvements in computing and technology demand the processing and analysis of huge datasets in a variety of ﬁelds. Often the analysis requires the… (more)

Subjects/Keywords: Compression; Image Patches; Nonnegative Matrix Factorization; Pixel Reordering; SVD; Applied Mathematics; Signal Processing

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APA (6^th Edition):

Charles, R. M. (2015). Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/68

Chicago Manual of Style (16^th Edition):

Charles, Richard Martin. “Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images.” 2015. Doctoral Dissertation, University of Colorado. Accessed March 06, 2021. https://scholar.colorado.edu/appm_gradetds/68.

MLA Handbook (7^th Edition):

Charles, Richard Martin. “Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images.” 2015. Web. 06 Mar 2021.

Vancouver:

Charles RM. Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2021 Mar 06]. Available from: https://scholar.colorado.edu/appm_gradetds/68.

Council of Science Editors:

Charles RM. Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images. [Doctoral Dissertation]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/appm_gradetds/68

Delft University of Technology

9. van Winden, Thijs (author). Intensity-Aware Rank Estimation for Dimensionality Reduction in Imaging Mass Spectrometry.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:c6ddb2ce-5551-4bd1-807a-a234486cfc9b

► Imaging Mass Spectrometry (IMS) is a spectral imaging technique, which enables detection of the spatial distribution of molecules by collecting a mass spectrum for every… (more)

▼ Imaging Mass Spectrometry (IMS) is a spectral imaging technique, which enables detection of the spatial distribution of molecules by collecting a mass spectrum for every pixel across a tissue sample. As such, IMS enables the detection of disease-introduced anomalies in tissue samples as well as the gaining of deeper insight on a molecular level into biological processes. The dimensionality of IMS data is high, considering that every bin (or ion) along a mass spectrum represents a separate image and the number of pixels per image is relatively high. Manual analysis of the data suffers from this high dimensionality as visualization becomes increasingly difficult. Furthermore, analysis of such large datasets becomes problematic or infeasible for computational techniques both in time and computational resources. Moreover, the dimensionality of current IMS measurements hampers new applications capturing even more data. Linear dimensionality reduction methods, such as Principal Component Analysis (PCA) and Nonnegative Matrix Factorization (NMF), seek to reduce these datasets to a set of (principal) components. These components span an underlying feature subspace within the original measurement space. Rank estimation determines the quantity of such components, estimating the number needed to represent the original dataset in a lower-dimensional space while incurring minimal information loss. In the context of IMS, this task is typically performed without the use of domain-specific knowledge. Intensity-aware rank estimation seeks to utilize domain knowledge - in the form of an ion intensity threshold - to help estimate the rank. This threshold emerges naturally from IMS, due to prior knowledge on instrument and ionization process inaccuracies in the low ion intensity region. The ion intensity threshold defines a lower bound for which variations in measurements are reliable. Establishing an intensity-aware version of rank estimation requires the threshold, defined in the original measurement space, to be linked to the abstract feature subspace, defined by NMF or PCA, where the rank estimation takes place. This connection is nontrivial to make and is, therefore, a central topic of this thesis. Furthermore, intensity-aware rank estimation requires the abstract subspace to represent the majority of the information above the threshold in the first set of components, which is not guaranteed in pure NMF and PCA formulations. In this thesis, we demonstrate threshold-aware rank estimation and residual-fraction rank estimation which make rank estimation for PCA intensity-aware. Threshold-aware rank estimation applies a histogram transformation to the intensities in the original measurement space to emphasize threshold-exceeding intensities. Consecutively, we estimate the rank based on the percentage of explained variance. Residual-fraction rank estimation uses untransformed measurements but instead estimates rank based on the ratio of the above- and below-threshold residuals. We demonstrate that both rank estimations are able to find… Advisors/Committee Members: van de Plas, Raf (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Imaging Mass Spectrometry; Dimensionality Reduction; Rank Estimation; Principal Component Analysis; Nonnegative Matrix Factorization

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APA (6^th Edition):

van Winden, T. (. (2019). Intensity-Aware Rank Estimation for Dimensionality Reduction in Imaging Mass Spectrometry. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c6ddb2ce-5551-4bd1-807a-a234486cfc9b

Chicago Manual of Style (16^th Edition):

van Winden, Thijs (author). “Intensity-Aware Rank Estimation for Dimensionality Reduction in Imaging Mass Spectrometry.” 2019. Masters Thesis, Delft University of Technology. Accessed March 06, 2021. http://resolver.tudelft.nl/uuid:c6ddb2ce-5551-4bd1-807a-a234486cfc9b.

MLA Handbook (7^th Edition):

van Winden, Thijs (author). “Intensity-Aware Rank Estimation for Dimensionality Reduction in Imaging Mass Spectrometry.” 2019. Web. 06 Mar 2021.

Vancouver:

van Winden T(. Intensity-Aware Rank Estimation for Dimensionality Reduction in Imaging Mass Spectrometry. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 06]. Available from: http://resolver.tudelft.nl/uuid:c6ddb2ce-5551-4bd1-807a-a234486cfc9b.

Council of Science Editors:

van Winden T(. Intensity-Aware Rank Estimation for Dimensionality Reduction in Imaging Mass Spectrometry. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:c6ddb2ce-5551-4bd1-807a-a234486cfc9b

10. Recoskie, Daniel. Constrained Nonnegative Matrix Factorization with Applications to Music Transcription.

Degree: 2014, University of Waterloo

URL: http://hdl.handle.net/10012/8639

► In this work we explore using nonnegative matrix factorization (NMF) for music transcription, as well as several other applications. NMF is an unsupervised learning method… (more)

Subjects/Keywords: nonnegative matrix factorization; music transcription

…matrix factorization, but here we focus on only one: nonnegative matrix factorization. Why only… …quantization (VQ), and nonnegative matrix factorization (NMF). The authors run… …quantization (VQ), and nonnegative matrix factorization (NMF). Shown in the… …transform of (a,b) respectively. 18 Chapter 3 Nonnegative Matrix Factorization 3.1… …Background The goal of nonnegative matrix factorization (NMF) is to approximate a matrix…

10 more images…

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APA (6^th Edition):

Recoskie, D. (2014). Constrained Nonnegative Matrix Factorization with Applications to Music Transcription. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8639

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Recoskie, Daniel. “Constrained Nonnegative Matrix Factorization with Applications to Music Transcription.” 2014. Thesis, University of Waterloo. Accessed March 06, 2021. http://hdl.handle.net/10012/8639.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Recoskie, Daniel. “Constrained Nonnegative Matrix Factorization with Applications to Music Transcription.” 2014. Web. 06 Mar 2021.

Vancouver:

Recoskie D. Constrained Nonnegative Matrix Factorization with Applications to Music Transcription. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10012/8639.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Recoskie D. Constrained Nonnegative Matrix Factorization with Applications to Music Transcription. [Thesis]. University of Waterloo; 2014. Available from: http://hdl.handle.net/10012/8639

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

11. Chou, Yung-Chieh. Automatic Term Explanation based on Topic-regularized Recurrent Neural Network.

Degree: Master, Information Management, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708118-120036

► In this study, we propose a topic-regularized Recurrent Neural Network(RNN)-based model designed to explain given terms. RNN-based models usually generate text results that have correct… (more)

Subjects/Keywords: Recurrent neural network; Automatic sentence generation; Automatic term explanation; Automatic summarization; Nonnegative matrix factorization; Topic model; Long short-term memory

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APA (6^th Edition):

Chou, Y. (2018). Automatic Term Explanation based on Topic-regularized Recurrent Neural Network. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708118-120036

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chou, Yung-Chieh. “Automatic Term Explanation based on Topic-regularized Recurrent Neural Network.” 2018. Thesis, NSYSU. Accessed March 06, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708118-120036.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chou, Yung-Chieh. “Automatic Term Explanation based on Topic-regularized Recurrent Neural Network.” 2018. Web. 06 Mar 2021.

Vancouver:

Chou Y. Automatic Term Explanation based on Topic-regularized Recurrent Neural Network. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Mar 06]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708118-120036.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chou Y. Automatic Term Explanation based on Topic-regularized Recurrent Neural Network. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708118-120036

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Brisebarre, Godefroy. Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach.

Degree: Docteur es, Optique, Photonique et Traitement d'Image, 2014, Ecole centrale de Marseille

URL: http://www.theses.fr/2014ECDM0010

►

L’imagerie hyperspectrale est un type d’imagerie émergent qui connaît un essor important depuis le début des années 2000. Grâce à une structure spectrale très fine… (more)

▼

L’imagerie hyperspectrale est un type d’imagerie émergent qui connaît un essor important depuis le début des années 2000. Grâce à une structure spectrale très fine qui produit un volume de donnée très important, elle apporte, par rapport à l’imagerie visible classique, un supplément d’information pouvant être mis à profit dans de nombreux domaines d’exploitation. Nous nous intéressons spécifiquement à la détection et l’analyse de changements entre deux images de la même scène, pour des applications orientées vers la défense.Au sein de ce manuscrit, nous commençons par présenter l’imagerie hyperspectrale et les contraintes associées à son utilisation pour des problématiques de défense. Nous présentons ensuite une méthode de détection et de classification de changements basée sur la recherche de directions spécifiques dans l’espace généré par le couple d’images, puis sur la fusion des directions proches. Nous cherchons ensuite à exploiter l’information obtenue sur les changements en nous intéressant aux possibilités de dé-mélange de séries temporelles d’images d’une même scène. Enfin, nous présentons un certain nombre d’extensions qui pourront être réalisées afin de généraliser ou améliorer les travaux présentés et nous concluons.

Hyperspectral imagery is an emerging imagery technology which has known a growing interest since the 2000’s. This technology allows an impressive growth of the data registered from a specific scene compared to classical RGB imagery. Indeed, although the spatial resolution is significantly lower, the spectral resolution is very small and the covered spectral area is very wide. We focus on change detection between two images of a given scene for defense oriented purposes.In the following, we start by introducing hyperspectral imagery and the specificity of its exploitation for defence purposes. We then present a change detection and analysis method based on the search for specifical directions in the space generated by the image couple, followed by a merging of the nearby directions. We then exploit this information focusing on theunmixing capabilities of multitemporal hyperspectral data. Finally, we will present a range of further works that could be done in relation with our work and conclude about it.

Advisors/Committee Members: Guillaume, Mireille (thesis director).

Subjects/Keywords: Classification de changements; Dé-mélange; Factorisation en matrice non-négatives; Change classification; Unmixing; Nonnegative Matrix Factorization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Brisebarre, G. (2014). Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach. (Doctoral Dissertation). Ecole centrale de Marseille. Retrieved from http://www.theses.fr/2014ECDM0010

Chicago Manual of Style (16^th Edition):

Brisebarre, Godefroy. “Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach.” 2014. Doctoral Dissertation, Ecole centrale de Marseille. Accessed March 06, 2021. http://www.theses.fr/2014ECDM0010.

MLA Handbook (7^th Edition):

Brisebarre, Godefroy. “Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach.” 2014. Web. 06 Mar 2021.

Vancouver:

Brisebarre G. Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach. [Internet] [Doctoral dissertation]. Ecole centrale de Marseille; 2014. [cited 2021 Mar 06]. Available from: http://www.theses.fr/2014ECDM0010.

Council of Science Editors:

Brisebarre G. Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach. [Doctoral Dissertation]. Ecole centrale de Marseille; 2014. Available from: http://www.theses.fr/2014ECDM0010

13. Cahill, Niall M. An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.

Degree: 2012, RIAN

URL: http://eprints.maynoothuniversity.ie/3988/

► In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to… (more)

▼ In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies…

Subjects/Keywords: Electronic Engineering; monaural sound source separation; nonnegative matrix factorization; acoustic echo; reverberation mitigation; hands-free telephony

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cahill, N. M. (2012). An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony. (Thesis). RIAN. Retrieved from http://eprints.maynoothuniversity.ie/3988/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cahill, Niall M. “An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.” 2012. Thesis, RIAN. Accessed March 06, 2021. http://eprints.maynoothuniversity.ie/3988/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cahill, Niall M. “An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.” 2012. Web. 06 Mar 2021.

Vancouver:

Cahill NM. An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony. [Internet] [Thesis]. RIAN; 2012. [cited 2021 Mar 06]. Available from: http://eprints.maynoothuniversity.ie/3988/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cahill NM. An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony. [Thesis]. RIAN; 2012. Available from: http://eprints.maynoothuniversity.ie/3988/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Cahill, Niall M. An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.

Degree: 2012, RIAN

URL: http://mural.maynoothuniversity.ie/3988/

► In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to… (more)

▼ In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies…

Subjects/Keywords: Electronic Engineering; monaural sound source separation; nonnegative matrix factorization; acoustic echo; reverberation mitigation; hands-free telephony

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cahill, N. M. (2012). An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony. (Thesis). RIAN. Retrieved from http://mural.maynoothuniversity.ie/3988/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cahill, Niall M. “An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.” 2012. Thesis, RIAN. Accessed March 06, 2021. http://mural.maynoothuniversity.ie/3988/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cahill, Niall M. “An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.” 2012. Web. 06 Mar 2021.

Vancouver:

Cahill NM. An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony. [Internet] [Thesis]. RIAN; 2012. [cited 2021 Mar 06]. Available from: http://mural.maynoothuniversity.ie/3988/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cahill NM. An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony. [Thesis]. RIAN; 2012. Available from: http://mural.maynoothuniversity.ie/3988/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Maryland

15. Tjoa, Steven Kiemyang. Sparse and Nonnegative Factorizations For Music Understanding.

Degree: Electrical Engineering, 2011, University of Maryland

URL: http://hdl.handle.net/1903/12072

► In this dissertation, we propose methods for sparse and nonnegative factorization that are specifically suited for analyzing musical signals. First, we discuss two constraints that… (more)

▼ In this dissertation, we propose methods for sparse and nonnegative factorization that are specifically suited for analyzing musical signals. First, we discuss two constraints that aid factorization of musical signals: harmonic and co-occurrence constraints. We propose a novel dictionary learning method that imposes harmonic constraints upon the atoms of the learned dictionary while allowing the dictionary size to grow appropriately during the learning procedure. When there is significant spectral-temporal overlap among the musical sources, our method outperforms popular existing matrix factorization methods as measured by the recall and precision of learned dictionary atoms. We also propose co-occurrence constraints – three simple and convenient multiplicative update rules for nonnegative matrix factorization (NMF) that enforce dependence among atoms. Using examples in music transcription, we demonstrate the ability of these updates to represent each musical note with multiple atoms and cluster the atoms for source separation purposes. Second, we study how spectral and temporal information extracted by nonnegative factorizations can improve upon musical instrument recognition. Musical instrument recognition in melodic signals is difficult, especially for classification systems that rely entirely upon spectral information instead of temporal information. Here, we propose a simple and effective method of combining spectral and temporal information for instrument recognition. While existing classification methods use traditional features such as statistical moments, we extract novel features from spectral and temporal atoms generated by NMF using a biologically motivated multiresolution gamma filterbank. Unlike other methods that require thresholds, safeguards, and hierarchies, the proposed spectral-temporal method requires only simple filtering and a flat classifier. Finally, we study how to perform sparse factorization when a large dictionary of musical atoms is already known. Sparse coding methods such as matching pursuit (MP) have been applied to problems in music information retrieval such as transcription and source separation with moderate success. However, when the set of dictionary atoms is large, identification of the best match in the dictionary with the residual is slow – linear in the size of the dictionary. Here, we propose a variant called approximate matching pursuit (AMP) that is faster than MP while maintaining scalability and accuracy. Unlike MP, AMP uses an approximate nearest-neighbor (ANN) algorithm to find the closest match in a dictionary in sublinear time. One such ANN algorithm, locality-sensitive hashing (LSH), is a probabilistic hash algorithm that places similar, yet not identical, observations into the same bin. While the accuracy of AMP is comparable to similar MP methods, the computational complexity is reduced. Also, by using LSH, this method scales easily; the dictionary can be expanded without reorganizing any data structures. Advisors/Committee Members: Liu, K. J. Ray (advisor).

Subjects/Keywords: Electrical engineering; Computer science; Music; Dictionary Learning; Music Information Retrieval; Music Transcription; Nonnegative Matrix Factorization; Source Separation; Sparse Coding

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tjoa, S. K. (2011). Sparse and Nonnegative Factorizations For Music Understanding. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/12072

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tjoa, Steven Kiemyang. “Sparse and Nonnegative Factorizations For Music Understanding.” 2011. Thesis, University of Maryland. Accessed March 06, 2021. http://hdl.handle.net/1903/12072.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tjoa, Steven Kiemyang. “Sparse and Nonnegative Factorizations For Music Understanding.” 2011. Web. 06 Mar 2021.

Vancouver:

Tjoa SK. Sparse and Nonnegative Factorizations For Music Understanding. [Internet] [Thesis]. University of Maryland; 2011. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1903/12072.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tjoa SK. Sparse and Nonnegative Factorizations For Music Understanding. [Thesis]. University of Maryland; 2011. Available from: http://hdl.handle.net/1903/12072

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

North Carolina State University

16. Lin, Matthew Min-Hsiung. Inverse Problems of Matrix Data Reconstruction.

Degree: PhD, Applied Mathematics, 2010, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/6260

► Mathematical modeling is an indispensable task in almost every discipline of sciences. If a model for a specific phenomenon can be correctly established, then it… (more)

▼ Mathematical modeling is an indispensable task in almost every discipline of sciences. If a model for a specific phenomenon can be correctly established, then it empowers the practitioners to analyze, predict, and delegate an onward decision which may have important applications and consequences. However, since most of the information gathering devices or methods, including our best intellectual endeavor for understanding, have only ï¬ nite bandwidth, we cannot avoid the fact that the models employed often are not exact. A constant update or modification of an existing model based on the newest information therefore is in demand. Such a task is generally referred to as an inverse problem. While in a forward problem the concern usually is to express the behavior of a certain physical system in terms of its system parameters, in the inverse problem the concern is to express the parameters in term of the behavior. This thesis addresses a small portion of the mass domain of inverse problems. The specific focus has been on matrix data reconstruction subject to some intrinsic or prescribed constraints. The purpose of this investigation is to develop theoretic understanding and numerical algorithms for model reconstruction so that the inexactness and uncertainty are reduced while certain specific conditions are satisfied. Explained and illustrated in this thesis are some most frequently used methodologies of matrix data reconstruction so that for a given dataset, these techniques can be employed to construct or update various (known) structural properties, or to classify or purify certain (unknown) embedded characteristics. Areas of applications include, for example, the applied mechanics where systems of bodies move in response to the values of their known endogenous parameters and the medical or social sciences where the causes (variables) of the observed incidences neither are known a priori nor can be precisely quantified. All of these could be considered as an inverse problem of matrix data reconstruction. This research revolves around two specific topics â€“ quadratic inverse eigenvalue problems and low rank approximations â€“ and some other related problems, both in theory and in computation. An immediate and the most straightforward application of the quadratic inverse eigenvalue problem would be the construction of a vibration system from its observed or desirable dynamical behavior. Its mathematical model is associated to the quadratic matrix polynomial Q(Î») = M Î»² + C Î» + K whose eigenvalues and eigenvectors govern the vibrational behavoir. Tremendous complexities and difficulties in recovering cofficient matrices M , C , K arise when the predetermined inner-connectivity among its elements and the mandatory nonnegativity of its parameters must be taken into account. Considerable efforts have been taken to derive theory and numerical methods for solving inverse eigenvalue problems, but techniques developed thus far can handle the inverse problems only on a case by case basis. The ï¬ rst… Advisors/Committee Members: Moody Chu, Committee Chair (advisor), Pierre Gremaud, Committee Member (advisor), Alina Chertock, Committee Member (advisor), Dmitry Zenkov, Committee Member (advisor).

Subjects/Keywords: nonnegative rank; eigenstructure completion; quadratic model; nonnegative rank factorization; Wedderburn rank reduction formula; inverse eigenvalue problem; quadratic matrix polynomial; model updating; spill-over; connectivity; linear inequality system; nonnegativity; low rank approximation; quadratic programming; maximin problem; semi-deï¬ nite programming; structural constraint; nonnegative matrix factorization; polytope approximation; Hahnâ€“Banach theorem; probability simplex; Euclidean distance matrix; pattern discovery; supporting hyperplane; matrix factorization; classiï¬ cation; clustering; nonnegative matrix; completely positive matrix; cp-rank

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lin, M. M. (2010). Inverse Problems of Matrix Data Reconstruction. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/6260

Chicago Manual of Style (16^th Edition):

Lin, Matthew Min-Hsiung. “Inverse Problems of Matrix Data Reconstruction.” 2010. Doctoral Dissertation, North Carolina State University. Accessed March 06, 2021. http://www.lib.ncsu.edu/resolver/1840.16/6260.

MLA Handbook (7^th Edition):

Lin, Matthew Min-Hsiung. “Inverse Problems of Matrix Data Reconstruction.” 2010. Web. 06 Mar 2021.

Vancouver:

Lin MM. Inverse Problems of Matrix Data Reconstruction. [Internet] [Doctoral dissertation]. North Carolina State University; 2010. [cited 2021 Mar 06]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/6260.

Council of Science Editors:

Lin MM. Inverse Problems of Matrix Data Reconstruction. [Doctoral Dissertation]. North Carolina State University; 2010. Available from: http://www.lib.ncsu.edu/resolver/1840.16/6260

17. Traa, Johannes. Phase difference and tensor factorization models for audio source separation.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/95277

► Audio source separation is a well-known problem in the speech community. Many methods have been proposed to isolate speech signals from a multichannel mixture. In… (more)

Subjects/Keywords: Nonnegative matrix factorization; Nonnegative tensor factorization; Interchannel phase differences; Audio Source Separation

…evaluated over D look directions in a nonnegative matrix L ∈ RD×F T and assume the factorization… …methods further and incorporate them into several matrix and tensor factorization algorithms… …significant drawback is that each term in the factorization contains its own F-by-T matrix of phase… …clearly many matrix and tensor factorization approaches to audio source separation. In this… …of the spline section satisfying (3.5) for fi . In matrix-vector form, we have…

15 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Traa, J. (2016). Phase difference and tensor factorization models for audio source separation. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95277

Chicago Manual of Style (16^th Edition):

Traa, Johannes. “Phase difference and tensor factorization models for audio source separation.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 06, 2021. http://hdl.handle.net/2142/95277.

MLA Handbook (7^th Edition):

Traa, Johannes. “Phase difference and tensor factorization models for audio source separation.” 2016. Web. 06 Mar 2021.

Vancouver:

Traa J. Phase difference and tensor factorization models for audio source separation. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/2142/95277.

Council of Science Editors:

Traa J. Phase difference and tensor factorization models for audio source separation. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95277

18. Feng, Fangchen. Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive.

Degree: Docteur es, Traitement du signal et des images, 2017, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2017SACLS232

►

La séparation aveugle de source consiste à estimer les signaux de sources uniquement à partir des mélanges observés. Le problème peut être séparé en deux… (more)

▼

La séparation aveugle de source consiste à estimer les signaux de sources uniquement à partir des mélanges observés. Le problème peut être séparé en deux catégories en fonction du modèle de mélange: mélanges instantanés, où le retard et la réverbération (effet multi-chemin) ne sont pas pris en compte, et des mélanges convolutives qui sont plus généraux mais plus compliqués. De plus, le bruit additif au niveaux des capteurs et le réglage sous-déterminé, où il y a moins de capteurs que les sources, rendent le problème encore plus difficile.Dans cette thèse, tout d'abord, nous avons étudié le lien entre deux méthodes existantes pour les mélanges instantanés: analyse des composants indépendants (ICA) et analyse des composant parcimonieux (SCA). Nous avons ensuite proposé une nouveau formulation qui fonctionne dans les cas déterminés et sous-déterminés, avec et sans bruit. Les évaluations numériques montrent l'avantage des approches proposées.Deuxièmement, la formulation proposés est généralisés pour les mélanges convolutifs avec des signaux de parole. En intégrant un nouveau modèle d'approximation, les algorithmes proposés fonctionnent mieux que les méthodes existantes, en particulier dans des scénarios bruyant et / ou de forte réverbération.Ensuite, on prend en compte la technique de décomposition morphologique et l'utilisation de parcimonie structurée qui conduit à des algorithmes qui peuvent mieux exploiter les structures des signaux audio. De telles approches sont testées pour des mélanges convolutifs sous-déterminés dans un scénario non-aveugle.Enfin, en bénéficiant du modèle NMF (factorisation en matrice non-négative), nous avons combiné l'hypothèse de faible-rang et de parcimonie et proposé de nouvelles approches pour les mélanges convolutifs sous-déterminés. Les expériences illustrent la bonne performance des algorithmes proposés pour les signaux de musique, en particulier dans des scénarios de forte réverbération.

Blind source separation (BSS) consists of estimating the source signals only from the observed mixtures. The problem can be divided into two categories according to the mixing model: instantaneous mixtures, where delay and reverberation (multi-path effect) are not taken into account, and convolutive mixtures which are more general but more complicated. Moreover, the additive noise at the sensor level and the underdetermined setting, where there are fewer sensors than the sources, make the problem even more difficult.In this thesis, we first studied the link between two existing methods for instantaneous mixtures: independent component analysis (ICA) and sparse component analysis (SCA). We then proposed a new formulation that works in both determined and underdetermined cases, with and without noise. Numerical evaluations show the advantage of the proposed approaches.Secondly, the proposed formulation is generalized for convolutive mixtures with speech signals. By integrating a new approximation model, the proposed algorithms work better than existing methods, especially in noisy and/or high…

Advisors/Committee Members: Kowalski, Matthieu (thesis director).

Subjects/Keywords: Séparation de sources; Représentation parcimonieuse; Transformée de Gabor; Factorisation en matrices non-négatives; Problèmes inverses; Optimisation; Source separation; Sparse representation; Gabor transform; Nonnegative matrix factorization; Inverse problem; Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Feng, F. (2017). Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2017SACLS232

Chicago Manual of Style (16^th Edition):

Feng, Fangchen. “Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive.” 2017. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 06, 2021. http://www.theses.fr/2017SACLS232.

MLA Handbook (7^th Edition):

Feng, Fangchen. “Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive.” 2017. Web. 06 Mar 2021.

Vancouver:

Feng F. Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2017. [cited 2021 Mar 06]. Available from: http://www.theses.fr/2017SACLS232.

Council of Science Editors:

Feng F. Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2017. Available from: http://www.theses.fr/2017SACLS232

19. Lefèvre, Augustin. Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur.

Degree: Docteur es, Mathématiques appliquées, 2012, Cachan, Ecole normale supérieure

URL: http://www.theses.fr/2012DENS0051

►

Nous proposons dans cette thèse trois contributions principales aux méthodes d'apprentissage de dictionnaire. La première est un critère de parcimonie par groupes adapté à la… (more)

▼

Nous proposons dans cette thèse trois contributions principales aux méthodes d'apprentissage de dictionnaire. La première est un critère de parcimonie par groupes adapté à la NMF lorsque la mesure de distorsion choisie est la divergence d'Itakura-Saito. Dans la plupart des signaux de musique on peut trouver de longs intervalles où seulement une source est active (des soli). Le critère de parcimonie par groupe que nous proposons permet de trouver automatiquement de tels segments et d'apprendre un dictionnaire adapté à chaque source. Ces dictionnaires permettent ensuite d'effectuer la tâche de séparation dans les intervalles où les sources sont mélangés. Ces deux tâches d'identification et de séparation sont effectuées simultanément en une seule passe de l'algorithme que nous proposons. Notre deuxième contribution est un algorithme en ligne pour apprendre le dictionnaire à grande échelle, sur des signaux de plusieurs heures. L'espace mémoire requis par une NMF estimée en ligne est constant alors qu'il croit linéairement avec la taille des signaux fournis dans la version standard, ce qui est impraticable pour des signaux de plus d'une heure. Notre troisième contribution touche à l'interaction avec l'utilisateur. Pour des signaux courts, l'apprentissage aveugle est particulièrement dificile, et l'apport d'information spécifique au signal traité est indispensable. Notre contribution est similaire à l'inpainting et permet de prendre en compte des annotations temps-fréquences. Elle repose sur l'observation que la quasi-totalité du spectrogramme peut etre divisé en régions spécifiquement assignées à chaque source. Nous décrivons une extension de NMF pour prendre en compte cette information et discutons la possibilité d'inférer cette information automatiquement avec des outils d'apprentissage statistique simples.

In this thesis we provide three main contributions to blind source separation methods based on NMF. Our first contribution is a group-sparsity inducing penalty specifically tailored for Itakura-Saito NMF. In many music tracks, there are whole intervals where only one source is active at the same time. The group-sparsity penalty we propose allows to blindly indentify these intervals and learn source specific dictionaries. As a consequence, those learned dictionaries can be used to do source separation in other parts of the track were several sources are active. These two tasks of identification and separation are performed simultaneously in one run of group-sparsity Itakura-Saito NMF. Our second contribution is an online algorithm for Itakura-Saito NMF that allows to learn dictionaries on very large audio tracks. Indeed, the memory complexity of a batch implementation NMF grows linearly with the length of the recordings and becomes prohibitive for signals longer than an hour. In contrast, our online algorithm is able to learn NMF on arbitrarily long signals with limited memory usage. Our third contribution deals user informed NMF. In short mixed signals, blind learning becomes very hard and sparsity do not retrieve…

Advisors/Committee Members: Bach, Francis (thesis director).

Subjects/Keywords: Apprentissage statistique; Factorisation en matrices positives; Normes structurées; Algorithme incrémental; Séparation de sources informée; Informed source separation; Incremental algorithms; Structured norms; Nonnegative matrix factorization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lefèvre, A. (2012). Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur. (Doctoral Dissertation). Cachan, Ecole normale supérieure. Retrieved from http://www.theses.fr/2012DENS0051

Chicago Manual of Style (16^th Edition):

Lefèvre, Augustin. “Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur.” 2012. Doctoral Dissertation, Cachan, Ecole normale supérieure. Accessed March 06, 2021. http://www.theses.fr/2012DENS0051.

MLA Handbook (7^th Edition):

Lefèvre, Augustin. “Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur.” 2012. Web. 06 Mar 2021.

Vancouver:

Lefèvre A. Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur. [Internet] [Doctoral dissertation]. Cachan, Ecole normale supérieure; 2012. [cited 2021 Mar 06]. Available from: http://www.theses.fr/2012DENS0051.

Council of Science Editors:

Lefèvre A. Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur. [Doctoral Dissertation]. Cachan, Ecole normale supérieure; 2012. Available from: http://www.theses.fr/2012DENS0051

Queensland University of Technology

20. Chen, Brenden Chong. Robust image hash functions using higher order spectra.

Degree: 2012, Queensland University of Technology

URL: https://eprints.qut.edu.au/61087/

► Robust hashing is an emerging field that can be used to hash certain data types in applications unsuitable for traditional cryptographic hashing methods. Traditional hashing… (more)

▼ Robust hashing is an emerging field that can be used to hash certain data types in applications unsuitable for traditional cryptographic hashing methods. Traditional hashing functions have been used extensively for data/message integrity, data/message authentication, efficient file identification and password verification. These applications are possible because the hashing process is compressive, allowing for efficient comparisons in the hash domain but non-invertible meaning hashes can be used without revealing the original data. These techniques were developed with deterministic (non-changing) inputs such as files and passwords. For such data types a 1-bit or one character change can be significant, as a result the hashing process is sensitive to any change in the input. Unfortunately, there are certain applications where input data are not perfectly deterministic and minor changes cannot be avoided. Digital images and biometric features are two types of data where such changes exist but do not alter the meaning or appearance of the input. For such data types cryptographic hash functions cannot be usefully applied. In light of this, robust hashing has been developed as an alternative to cryptographic hashing and is designed to be robust to minor changes in the input. Although similar in name, robust hashing is fundamentally different from cryptographic hashing. Current robust hashing techniques are not based on cryptographic methods, but instead on pattern recognition techniques. Modern robust hashing algorithms consist of feature extraction followed by a randomization stage that introduces non-invertibility and compression, followed by quantization and binary encoding to produce a binary hash output. In order to preserve robustness of the extracted features, most randomization methods are linear and this is detrimental to the security aspects required of hash functions. Furthermore, the quantization and encoding stages used to binarize real-valued features requires the learning of appropriate quantization thresholds. How these thresholds are learnt has an important effect on hashing accuracy and the mere presence of such thresholds are a source of information leakage that can reduce hashing security. This dissertation outlines a systematic investigation of the quantization and encoding stages of robust hash functions. While existing literature has focused on the importance of quantization scheme, this research is the first to emphasise the importance of the quantizer training on both hashing accuracy and hashing security. The quantizer training process is presented in a statistical framework which allows a theoretical analysis of the effects of quantizer training on hashing performance. This is experimentally verified using a number of baseline robust image hashing algorithms over a large database of real world images. This dissertation also proposes a new randomization method for robust image hashing based on Higher Order Spectra (HOS) and Radon projections. The method is non-linear and this is…

Subjects/Keywords: robust hashing; image hashing; higher order spectra; bispectrum; adaptive deterministic quantization; random projection; biometric template security; nonnegative matrix factorization; Fourier-Mellin; Gray code; distance distortion

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, B. C. (2012). Robust image hash functions using higher order spectra. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/61087/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Brenden Chong. “Robust image hash functions using higher order spectra.” 2012. Thesis, Queensland University of Technology. Accessed March 06, 2021. https://eprints.qut.edu.au/61087/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Brenden Chong. “Robust image hash functions using higher order spectra.” 2012. Web. 06 Mar 2021.

Vancouver:

Chen BC. Robust image hash functions using higher order spectra. [Internet] [Thesis]. Queensland University of Technology; 2012. [cited 2021 Mar 06]. Available from: https://eprints.qut.edu.au/61087/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen BC. Robust image hash functions using higher order spectra. [Thesis]. Queensland University of Technology; 2012. Available from: https://eprints.qut.edu.au/61087/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Université de Lorraine

21. Vo, Xuan Thanh. Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization.

Degree: Docteur es, Informatique, 2015, Université de Lorraine

URL: http://www.theses.fr/2015LORR0193

►

Dans cette thèse, nous nous concentrons sur le développement des méthodes d'optimisation pour résoudre certaines classes de problèmes d'apprentissage avec la parcimonie et/ou avec l'incertitude… (more)

▼

Dans cette thèse, nous nous concentrons sur le développement des méthodes d'optimisation pour résoudre certaines classes de problèmes d'apprentissage avec la parcimonie et/ou avec l'incertitude des données. Nos méthodes sont basées sur la programmation DC (Difference of Convex functions) et DCA (DC Algorithms) étant reconnues comme des outils puissants d'optimisation. La thèse se compose de deux parties : La première partie concerne la parcimonie tandis que la deuxième partie traite l'incertitude des données. Dans la première partie, une étude approfondie pour la minimisation de la norme zéro a été réalisée tant sur le plan théorique qu'algorithmique. Nous considérons une approximation DC commune de la norme zéro et développons quatre algorithmes basées sur la programmation DC et DCA pour résoudre le problème approché. Nous prouvons que nos algorithmes couvrent tous les algorithmes standards existants dans le domaine. Ensuite, nous étudions le problème de la factorisation en matrices non-négatives (NMF) et fournissons des algorithmes appropriés basés sur la programmation DC et DCA. Nous étudions également le problème de NMF parcimonieuse. Poursuivant cette étude, nous étudions le problème d'apprentissage de dictionnaire où la représentation parcimonieuse joue un rôle crucial. Dans la deuxième partie, nous exploitons la technique d'optimisation robuste pour traiter l'incertitude des données pour les deux problèmes importants dans l'apprentissage : la sélection de variables dans SVM (Support Vector Machines) et le clustering. Différents modèles d'incertitude sont étudiés. Les algorithmes basés sur DCA sont développés pour résoudre ces problèmes.

In this thesis, we focus on developing optimization approaches for solving some classes of optimization problems in sparsity and robust optimization for data uncertainty. Our methods are based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) which are well-known as powerful tools in optimization. This thesis is composed of two parts: the first part concerns with sparsity while the second part deals with uncertainty. In the first part, a unified DC approximation approach to optimization problem involving the zero-norm in objective is thoroughly studied on both theoretical and computational aspects. We consider a common DC approximation of zero-norm that includes all standard sparse inducing penalty functions, and develop general DCA schemes that cover all standard algorithms in the field. Next, the thesis turns to the nonnegative matrix factorization (NMF) problem. We investigate the structure of the considered problem and provide appropriate DCA based algorithms. To enhance the performance of NMF, the sparse NMF formulations are proposed. Continuing this topic, we study the dictionary learning problem where sparse representation plays a crucial role. In the second part, we exploit robust optimization technique to deal with data uncertainty for two important problems in machine learning: feature selection in linear Support Vector Machines and…

Advisors/Committee Members: Lê Thi, Hoai An (thesis director).

Subjects/Keywords: Optimisation robuste; Programmation DC; DCA; Parcimonie; Factorisation en matrices non-Négatives; Apprentissage de dictionnaire; Robust optimization; DC programming; DCA; Sparsity; Nonnegative matrix factorization; Dictionary learning; 004.015 1; 511.8

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vo, X. T. (2015). Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization. (Doctoral Dissertation). Université de Lorraine. Retrieved from http://www.theses.fr/2015LORR0193

Chicago Manual of Style (16^th Edition):

Vo, Xuan Thanh. “Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization.” 2015. Doctoral Dissertation, Université de Lorraine. Accessed March 06, 2021. http://www.theses.fr/2015LORR0193.

MLA Handbook (7^th Edition):

Vo, Xuan Thanh. “Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization.” 2015. Web. 06 Mar 2021.

Vancouver:

Vo XT. Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization. [Internet] [Doctoral dissertation]. Université de Lorraine; 2015. [cited 2021 Mar 06]. Available from: http://www.theses.fr/2015LORR0193.

Council of Science Editors:

Vo XT. Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization. [Doctoral Dissertation]. Université de Lorraine; 2015. Available from: http://www.theses.fr/2015LORR0193

22. Mei, Jiali. Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique.

Degree: Docteur es, Mathématiques appliquées, 2017, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2017SACLS578

►

Nous sommes intéressé par la reconstitution et la prédiction des séries temporelles multivariées à partir des données partiellement observées et/ou agrégées.La motivation du problème vient… (more)

▼

Nous sommes intéressé par la reconstitution et la prédiction des séries temporelles multivariées à partir des données partiellement observées et/ou agrégées.La motivation du problème vient des applications dans la gestion du réseau électrique.Nous envisageons des outils capables de résoudre le problème d'estimation de plusieurs domaines.Après investiguer le krigeage, qui est une méthode de la litérature de la statistique spatio-temporelle, et une méthode hybride basée sur le clustering des individus, nous proposons un cadre général de reconstitution et de prédiction basé sur la factorisation de matrice nonnégative.Ce cadre prend en compte de manière intrinsèque la corrélation entre les séries temporelles pour réduire drastiquement la dimension de l'espace de paramètres.Une fois que le problématique est formalisé dans ce cadre, nous proposons deux extensions par rapport à l'approche standard.La première extension prend en compte l'autocorrélation temporelle des individus.Cette information supplémentaire permet d'améliorer la précision de la reconstitution.La deuxième extension ajoute une composante de régression dans la factorisation de matrice nonnégative.Celle-ci nous permet d'utiliser dans l'estimation du modèle des variables exogènes liées avec la consommation électrique, ainsi de produire des facteurs plus interprétatbles, et aussi améliorer la reconstitution.De plus, cette méthod nous donne la possibilité d'utiliser la factorisation de matrice nonnégative pour produire des prédictions.Sur le côté théorique, nous nous intéressons à l'identifiabilité du modèle, ainsi qu'à la propriété de la convergence des algorithmes que nous proposons.La performance des méthodes proposées en reconstitution et en prédiction est testé sur plusieurs jeux de données de consommation électrique à niveaux d'agrégation différents.

We are interested in the recovery and prediction of multiple time series from partially observed and/or aggregate data.Motivated by applications in electricity network management, we investigate tools from multiple fields that are able to deal with such data issues.After examining kriging from spatio-temporal statistics and a hybrid method based on the clustering of individuals, we propose a general framework based on nonnegative matrix factorization.This frameworks takes advantage of the intrisic correlation between the multivariate time series to greatly reduce the dimension of the parameter space.Once the estimation problem is formalized in the nonnegative matrix factorization framework, two extensions are proposed to improve the standard approach.The first extension takes into account the individual temporal autocorrelation of each of the time series.This increases the precision of the time series recovery.The second extension adds a regression layer into nonnegative matrix factorization.This allows exogenous variables that are known to be linked with electricity consumption to be used in estimation, hence makes the factors obtained by the method to be more interpretable, and also increases the recovery…

Advisors/Committee Members: Castro, Yohann de (thesis director), Hébrail, Georges (thesis director).

Subjects/Keywords: Analyse spatiale; Séries chronologiques; Consommation électrique; Séparation de sources; Factorisation de matrice nonnégative; Spatial analysis; Times series; Electricity consumption; Source separation; Nonnegative matrix factorization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mei, J. (2017). Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2017SACLS578

Chicago Manual of Style (16^th Edition):

Mei, Jiali. “Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique.” 2017. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 06, 2021. http://www.theses.fr/2017SACLS578.

MLA Handbook (7^th Edition):

Mei, Jiali. “Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique.” 2017. Web. 06 Mar 2021.

Vancouver:

Mei J. Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2017. [cited 2021 Mar 06]. Available from: http://www.theses.fr/2017SACLS578.

Council of Science Editors:

Mei J. Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2017. Available from: http://www.theses.fr/2017SACLS578

23. LIU ZHAOQIANG. THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION.

Degree: 2017, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/138222

Subjects/Keywords: clustering; k-means; mixture models; dimensionality reduction; error bounds; nonnegative matrix factorization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

ZHAOQIANG, L. (2017). THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/138222

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

ZHAOQIANG, LIU. “THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION.” 2017. Thesis, National University of Singapore. Accessed March 06, 2021. http://scholarbank.nus.edu.sg/handle/10635/138222.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

ZHAOQIANG, LIU. “THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION.” 2017. Web. 06 Mar 2021.

Vancouver:

ZHAOQIANG L. THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION. [Internet] [Thesis]. National University of Singapore; 2017. [cited 2021 Mar 06]. Available from: http://scholarbank.nus.edu.sg/handle/10635/138222.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

ZHAOQIANG L. THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION. [Thesis]. National University of Singapore; 2017. Available from: http://scholarbank.nus.edu.sg/handle/10635/138222

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Maddali, Vinay. Speech denoising using nonnegative matrix factorization and neural networks.

Degree: MS, Electrical & Computer Engineering, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/88986

► The main goal of this research is to do source separation of single-channel mixed signals such that we get a clean representation of each source.… (more)

Subjects/Keywords: Nonnegative matrix factorization; neural networks; speech denoising; source separation

…denoising: nonnegative matrix factorization and neural networks. We have shown the performance of… …CHAPTER 2 NONNEGATIVE MATRIX FACTORIZATION 2.1 NMF INTRODUCTION The first method we used to… …solve this problem of speech denoising is one using nonnegative matrix factorization (NMF… …MODIFIED NMF ALGORITHM Nonnegative matrix factorization (NMF) and probabilistic latent… …modified approach as Block Kullback-Liebler nonnegative matrix factorization (BKL-NMF)…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Maddali, V. (2015). Speech denoising using nonnegative matrix factorization and neural networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88986

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Maddali, Vinay. “Speech denoising using nonnegative matrix factorization and neural networks.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed March 06, 2021. http://hdl.handle.net/2142/88986.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Maddali, Vinay. “Speech denoising using nonnegative matrix factorization and neural networks.” 2015. Web. 06 Mar 2021.

Vancouver:

Maddali V. Speech denoising using nonnegative matrix factorization and neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/2142/88986.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Maddali V. Speech denoising using nonnegative matrix factorization and neural networks. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/88986

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

25. Traitruengsakul, Supachan. Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms.

Degree: MS, Electrical Engineering, 2015, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/8867

► Infantile Spasms (ISS) characterized by electroencephalogram (EEG) recordings exhibiting hypsarrythmia (HYPS) are a severe form of epilepsy. Many clinicians have been trying to improve… (more)

▼ Infantile Spasms (ISS) characterized by electroencephalogram (EEG) recordings exhibiting hypsarrythmia (HYPS) are a severe form of epilepsy. Many clinicians have been trying to improve ISS outcomes; however, quantification of discharges from hypsarrythmic EEG readings remains challenging. This thesis describes the development of a novel method that assists clinicians to successfully localize the epileptic discharges associated with ISS in HYPS. The approach includes: construct the time-frequency domain (TFD) of the EEG recording using matching pursuit TFD (MP-TFD), decompose the TFD matrix into two submatrices using nonnegative matrix factorizations (NMF), and employ the decomposed vectors to locate the spikes. The proposed method was employed to an EEG dataset of five ISS individuals, and identification of spikes was compared with those which were identified by the epileptologists and those obtained using clinical software (Persyst). Performance evaluations showed results based on classification techniques: thresholdings, and support vector machine (SVM). Using the thresholdings, average true positive (TP) and false negative (FN) percentages of 86% and 14% were achieved, which represented a significant improvement over the use of Persyst, which only achieved average TP and FN percentages of 4% and 96%, respectively. Using SVM, the percentage of area under curve (AUC) of receiver operating characteristic (ROC) was significantly improved up to 98.56%. In summary, the proposed novel algorithm based on MP-TFD and NMF was able to successfully detect the epileptic discharges from the dataset. The development of the proposed automated method can potentially assist clinicians to successfully localize the epileptic discharges associated with ISS in HYPS. The quantitative assessment of spike detection, as well as other features of HYPS, is expected to allow a more accurate assessment of the relevance of EEG to clinical outcomes, which is significant in therapy management of ISS. Advisors/Committee Members: Behnaz Ghoraani.

Subjects/Keywords: Classification; Feature extraction; Hypsarrythmia; Nonnegative matrix factorization; Time-frequency representations

…submatrices using nonnegative matrix factorizations (NMF), and employ the decomposed… …x28;TFD) analysis and non-negative matrix factorization (NMF) to extract TF… …Analysis (PCA), and Non-Negative Matrix Factorization (NMF). Their common… …recording using matching pursuit TFD (MP-TFD), decompose the TFD matrix into two… …Representations 2.2 Part B: Matrix Decomposition . . . . . . . . . . . . . . . . . 2.3 Part C: Feature…

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APA (6^th Edition):

Traitruengsakul, S. (2015). Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8867

Chicago Manual of Style (16^th Edition):

Traitruengsakul, Supachan. “Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms.” 2015. Masters Thesis, Rochester Institute of Technology. Accessed March 06, 2021. https://scholarworks.rit.edu/theses/8867.

MLA Handbook (7^th Edition):

Traitruengsakul, Supachan. “Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms.” 2015. Web. 06 Mar 2021.

Vancouver:

Traitruengsakul S. Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms. [Internet] [Masters thesis]. Rochester Institute of Technology; 2015. [cited 2021 Mar 06]. Available from: https://scholarworks.rit.edu/theses/8867.

Council of Science Editors:

Traitruengsakul S. Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms. [Masters Thesis]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8867

University of Vienna

26. Janecek, Andreas. Efficient feature reduction and classification methods.

Degree: 2009, University of Vienna

URL: http://othes.univie.ac.at/8205/

► Durch die steigende Anzahl verfügbarer Daten in unterschiedlichsten Anwendungsgebieten nimmt der Aufwand vieler Data-Mining Applikationen signifikant zu. Speziell hochdimensionierte Daten (Daten die über viele verschiedene… (more)

▼ Durch die steigende Anzahl verfügbarer Daten in unterschiedlichsten Anwendungsgebieten nimmt der Aufwand vieler Data-Mining Applikationen signifikant zu. Speziell hochdimensionierte Daten (Daten die über viele verschiedene Attribute beschrieben werden) können ein großes Problem für viele Data-Mining Anwendungen darstellen. Neben höheren Laufzeiten können dadurch sowohl für überwachte (supervised), als auch nicht überwachte (unsupervised) Klassifikationsalgorithmen weitere Komplikationen entstehen (z.B. ungenaue Klassifikationsgenauigkeit, schlechte Clustering-Eigenschaften, …). Dies führt zu einem Bedarf an effektiven und effizienten Methoden zur Dimensionsreduzierung. Feature Selection (die Auswahl eines Subsets von Originalattributen) und Dimensionality Reduction (Transformation von Originalattribute in (Linear)-Kombinationen der Originalattribute) sind zwei wichtige Methoden um die Dimension von Daten zu reduzieren. Obwohl sich in den letzten Jahren vielen Studien mit diesen Methoden beschäftigt haben, gibt es immer noch viele offene Fragestellungen in diesem Forschungsgebiet. Darüber hinaus ergeben sich in vielen Anwendungsbereichen durch die immer weiter steigende Anzahl an verfügbaren und verwendeten Attributen und Features laufend neue Probleme. Das Ziel dieser Dissertation ist es, verschiedene Fragenstellungen in diesem Bereich genau zu analysieren und Verbesserungsmöglichkeiten zu entwickeln. Grundsätzlich, werden folgende Ansprüche an Methoden zur Feature Selection und Dimensionality Reduction gestellt: Die Methoden sollten effizient (bezüglich ihres Rechenaufwandes) sein und die resultierenden Feature-Sets sollten die Originaldaten möglichst kompakt repräsentieren können. Darüber hinaus ist es in vielen Anwendungsgebieten wichtig, die Interpretierbarkeit der Originaldaten beizubehalten. Letztendlich sollte der Prozess der Dimensionsreduzierung keinen negativen Effekt auf die Klassifikationsgenauigkeit haben - sondern idealerweise, diese noch verbessern. Offene Problemstellungen in diesem Bereich betreffen unter anderem den Zusammenhang zwischen Methoden zur Dimensionsreduzierung und der resultierenden Klassifikationsgenauigkeit, wobei sowohl eine möglichst kompakte Repräsentation der Daten, als auch eine hohe Klassifikationsgenauigkeit erzielt werden sollen. Wie bereits erwähnt, ergibt sich durch die große Anzahl an Daten auch ein erhöhter Rechenaufwand, weshalb schnelle und effektive Methoden zur Dimensionsreduzierung entwickelt werden müssen, bzw. existierende Methoden verbessert werden müssen. Darüber hinaus sollte natürlich auch der Rechenaufwand der verwendeten Klassifikationsmethoden möglichst gering sein. Des Weiteren ist die Interpretierbarkeit von Feature Sets zwar möglich, wenn Feature Selection Methoden für die Dimensionsreduzierung verwendet werden, im Fall von Dimensionality Reduction sind die resultierenden Feature Sets jedoch meist Linearkombinationen der Originalfeatures. Daher ist es schwierig zu überprüfen, wie viel Information einzelne Originalfeatures beitragen. Im Rahmen…

Subjects/Keywords: 54.72 Künstliche Intelligenz; 54.99 Informatik: Sonstiges; Feature Selection / Feature Reduction / Dimensionality Reduction / Klassifikation / NMF / Nonnegative Matrix Factorization; Feature reduction / dimensionality reduction / classification / NMF / nonnegative matrix factorization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Janecek, A. (2009). Efficient feature reduction and classification methods. (Thesis). University of Vienna. Retrieved from http://othes.univie.ac.at/8205/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Janecek, Andreas. “Efficient feature reduction and classification methods.” 2009. Thesis, University of Vienna. Accessed March 06, 2021. http://othes.univie.ac.at/8205/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Janecek, Andreas. “Efficient feature reduction and classification methods.” 2009. Web. 06 Mar 2021.

Vancouver:

Janecek A. Efficient feature reduction and classification methods. [Internet] [Thesis]. University of Vienna; 2009. [cited 2021 Mar 06]. Available from: http://othes.univie.ac.at/8205/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Janecek A. Efficient feature reduction and classification methods. [Thesis]. University of Vienna; 2009. Available from: http://othes.univie.ac.at/8205/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

27. Lorenz, Florian M. Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization.

Degree: MA, 0338, 2010, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/16196

► Nonnegative matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) are widely used in the physical sciences; this thesis explores the often-overlooked origins of… (more)

Subjects/Keywords: Nonnegative Matrix Factorization (NMF); Sequential Fitting (SEFIT); Alternating Least Squares (ALS); Nonnegative Least Squares (NNLS)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lorenz, F. M. (2010). Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/16196

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lorenz, Florian M. “Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization.” 2010. Thesis, University of Illinois – Urbana-Champaign. Accessed March 06, 2021. http://hdl.handle.net/2142/16196.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lorenz, Florian M. “Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization.” 2010. Web. 06 Mar 2021.

Vancouver:

Lorenz FM. Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2010. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/2142/16196.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lorenz FM. Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization. [Thesis]. University of Illinois – Urbana-Champaign; 2010. Available from: http://hdl.handle.net/2142/16196

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

28. Kuang, Da. Nonnegative matrix factorization for clustering.

Degree: PhD, Computational Science and Engineering, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/52299

► This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and efficient clustering method. Clustering is one of the fundamental tasks… (more)

▼ This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and efficient clustering method. Clustering is one of the fundamental tasks in machine learning. It is useful for unsupervised knowledge discovery in a variety of applications such as text mining and genomic analysis. NMF is a dimension reduction method that approximates a nonnegative matrix by the product of two lower rank nonnegative matrices, and has shown great promise as a clustering method when a data set is represented as a nonnegative data matrix. However, challenges in the widespread use of NMF as a clustering method lie in its correctness and efficiency: First, we need to know why and when NMF could detect the true clusters and guarantee to deliver good clustering quality; second, existing algorithms for computing NMF are expensive and often take longer time than other clustering methods. We show that the original NMF can be improved from both aspects in the context of clustering. Our new NMF-based clustering methods can achieve better clustering quality and run orders of magnitude faster than the original NMF and other clustering methods. Like other clustering methods, NMF places an implicit assumption on the cluster structure. Thus, the success of NMF as a clustering method depends on whether the representation of data in a vector space satisfies that assumption. Our approach to extending the original NMF to a general clustering method is to switch from the vector space representation of data points to a graph representation. The new formulation, called Symmetric NMF, takes a pairwise similarity matrix as an input and can be viewed as a graph clustering method. We evaluate this method on document clustering and image segmentation problems and find that it achieves better clustering accuracy. In addition, for the original NMF, it is difficult but important to choose the right number of clusters. We show that the widely-used consensus NMF in genomic analysis for choosing the number of clusters have critical flaws and can produce misleading results. We propose a variation of the prediction strength measure arising from statistical inference to evaluate the stability of clusters and select the right number of clusters. Our measure shows promising performances in artificial simulation experiments. Large-scale applications bring substantial efficiency challenges to existing algorithms for computing NMF. An important example is topic modeling where users want to uncover the major themes in a large text collection. Our strategy of accelerating NMF-based clustering is to design algorithms that better suit the computer architecture as well as exploit the computing power of parallel platforms such as the graphic processing units (GPUs). A key observation is that applying rank-2 NMF that partitions a data set into two clusters in a recursive manner is much faster than applying the original NMF to obtain a flat clustering. We take advantage of a special property of rank-2 NMF and design an algorithm that runs faster… Advisors/Committee Members: Park, Haesun (advisor), Chau, Duen Horng (Polo) (committee member), Saltz, Joel (committee member), Vuduc, Richard (committee member), Zhou, Hao-Min (committee member).

Subjects/Keywords: Nonnegative matrix factorization; Cluster analysis; Hierarchical clustering; Cancer subtype discovery; GPU computing; Sparse matrix multiplication

…128 xii SUMMARY This dissertation shows that nonnegative matrix factorization (NMF… …Factorization In nonnegative matrix factorization, given a nonnegative matrix X ∈ Rm×n and k ≤ + min… …method that approximates a nonnegative matrix by the product of two lower rank nonnegative… …nonnegative data matrix. However, challenges in the widespread use of NMF as a clustering method lie… …documents. xiv CHAPTER I INTRODUCTION This dissertation shows that nonnegative matrix…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kuang, D. (2014). Nonnegative matrix factorization for clustering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52299

Chicago Manual of Style (16^th Edition):

Kuang, Da. “Nonnegative matrix factorization for clustering.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 06, 2021. http://hdl.handle.net/1853/52299.

MLA Handbook (7^th Edition):

Kuang, Da. “Nonnegative matrix factorization for clustering.” 2014. Web. 06 Mar 2021.

Vancouver:

Kuang D. Nonnegative matrix factorization for clustering. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1853/52299.

Council of Science Editors:

Kuang D. Nonnegative matrix factorization for clustering. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52299

29. Nguyen, Duy Khuong. 非負数マトリックス因数分解へのリッチモデルと高速アルゴリズム.

Degree: 博士（知識科学）, 2016, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学

URL: http://hdl.handle.net/10119/13512

Supervisor:Ho Bao Tu

知識科学研究科

博士

Subjects/Keywords: Rich models; fast algorithms; nonnegative matrix factorization; parallel and distributed; Frobenius norm; KL divergence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Nguyen, D. K. (2016). 非負数マトリックス因数分解へのリッチモデルと高速アルゴリズム. (Thesis). Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10119/13512

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nguyen, Duy Khuong. “非負数マトリックス因数分解へのリッチモデルと高速アルゴリズム.” 2016. Thesis, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Accessed March 06, 2021. http://hdl.handle.net/10119/13512.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nguyen, Duy Khuong. “非負数マトリックス因数分解へのリッチモデルと高速アルゴリズム.” 2016. Web. 06 Mar 2021.

Vancouver:

Nguyen DK. 非負数マトリックス因数分解へのリッチモデルと高速アルゴリズム. [Internet] [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; 2016. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/10119/13512.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nguyen DK. 非負数マトリックス因数分解へのリッチモデルと高速アルゴリズム. [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; 2016. Available from: http://hdl.handle.net/10119/13512

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

30. Warmsley, Dana. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media.

Degree: PhD, Applied Mathematics, 2017, Cornell University

URL: http://hdl.handle.net/1813/59146

► The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups… (more)

▼ The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups in online social media. Beginning with a unique typology for detecting abusive language, we outline the distinctions and similarities of different abusive language subtasks (offensive language, hate speech, cyberbullying and trolling) and how we might benefit from the progress made in each area. Specifically, we suggest that each subtask can be categorized based on whether or not the abusive language being studied 1) is directed at a specific individual, or targets a generalized ``Other" and 2) the extent to which the language is explicit versus implicit. We then use knowledge gained from this typology to tackle the ``problem of offensive language" in hate speech detection. A key challenge for automated hate speech detection on social media is the separation of hate speech from other instances of offensive language. We present a Logistic Regression classifier, trained on human annotated Twitter data, that makes use of a uniquely derived lexicon of hate terms along with features that have proven successful in the detection of offensive language, hate speech and cyberbullying. Using the tweets classified by the aforementioned hate speech classifier, we extract a set of users for which we collect demographic and psychological attributes, with the goal of understanding how these attributes are related to hate speech use. We first present a binary Random Forest classifier for predicting whether or not a Twitter user is a hate speaker. We then explore the use of linear and Random Forest regression models as a means of explaining and predicting levels of hate speech use based on user attributes. To the best of my knowledge, this work is the first to present an automated approach for detecting individual hate speakers. Finally, we present a non-negative matrix factorization (NMF) algorithm for identifying polarized groups using tripartite graphs (user-post-tag) gleaned from social media data. This work is heavily inspired by the need for an unsupervised approach that works well in contexts varying in the nature of the controversy, the level of polarization, the number of polarity groups involved, and the presence of neutral entities. I present the first ever analysis of polarization on data from the Tumblr platform, showing improved performance over traditional community detection methods and the state-of-the-art method of NMF on bipartite graphs. Advisors/Committee Members: Strogatz, Steven H. (chair), Macy, Michael Walton (committee member), Rand, Richard Herbert (committee member), Lewis, Mark E. (committee member).

Subjects/Keywords: hate speech; nonnegative matrix factorization; polarization; Classification; Applied mathematics; Computer science; Sociology; machine learning

…5.2 5.3 5.4 5.5 5.6 5.7 5.8 Nonnegative Matrix Factorization Algorithm Notation Nonnegative… …5.2 Related Work . . . . . . . . . . . . . . . . . . . . . 5.3 A Nonnegative Matrix… …Matrix Factorization Algorithm . . . . . Polarization Dataset Statistics… …Finally, chapter 5 presents a non-negative matrix factorization algorithm for uncovering… …Factorization Approach . . 5.3.1 NMF on a Tripartite Graph . . . . . . . . . 5.3.2 Regularization…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Warmsley, D. (2017). On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59146

Chicago Manual of Style (16^th Edition):

Warmsley, Dana. “On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media.” 2017. Doctoral Dissertation, Cornell University. Accessed March 06, 2021. http://hdl.handle.net/1813/59146.

MLA Handbook (7^th Edition):

Warmsley, Dana. “On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media.” 2017. Web. 06 Mar 2021.

Vancouver:

Warmsley D. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2021 Mar 06]. Available from: http://hdl.handle.net/1813/59146.

Council of Science Editors:

Warmsley D. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/59146

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Open Access Theses and Dissertations