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You searched for subject:(Nonnegative Matrix Factorization). Showing records 1 – 30 of 43 total matches.

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Northeastern University

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

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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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… 

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

 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)

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; Hahn–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 (6th 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 (16th 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 (7th 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

 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… 

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

APA (6th 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 (16th 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 (7th 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)

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)

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 (6th 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 (16th 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 (7th 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

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)

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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

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)

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 (6th 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 (16th 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 (7th 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)

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)

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 (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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)

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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

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

Page 1 Page 2 Page 3

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APA (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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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