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You searched for subject:(nonnegative matrix). Showing records 1 – 30 of 46 total matches.

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University of Minnesota

1. 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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Mosesov A. Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization. [Internet] [Masters thesis]. University of Minnesota; 2014. [cited 2019 May 21]. 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 Illinois – Urbana-Champaign

2. 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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Lorenz FM. Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2010. [cited 2019 May 21]. 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


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 May 21, 2019. 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. 21 May 2019.

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 2019 May 21]. 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


University of Manchester

4. Lin, Lijing. Roots of stochastic matrices and fractional matrix powers.

Degree: PhD, 2011, University of Manchester

 In Markov chain models in finance and healthcare a transition matrix over a certain time interval is needed but only a transition matrix over a… (more)

Subjects/Keywords: 512.9; Stochastic matrix; nonnegative matrix; matrix $p$th root; primary matrix function; nonprimary matrix function; Perron – Frobenius theorem; Markov chain; transition matrix; embeddability problem; $M$-matrix; inverse eigenvalue problem

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APA (6th Edition):

Lin, L. (2011). Roots of stochastic matrices and fractional matrix powers. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/roots-of-stochastic-matrices-and-fractional-matrix-powers(3f7dbb69-7c22-4fe9-9461-429c25c0db85).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528523

Chicago Manual of Style (16th Edition):

Lin, Lijing. “Roots of stochastic matrices and fractional matrix powers.” 2011. Doctoral Dissertation, University of Manchester. Accessed May 21, 2019. https://www.research.manchester.ac.uk/portal/en/theses/roots-of-stochastic-matrices-and-fractional-matrix-powers(3f7dbb69-7c22-4fe9-9461-429c25c0db85).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528523.

MLA Handbook (7th Edition):

Lin, Lijing. “Roots of stochastic matrices and fractional matrix powers.” 2011. Web. 21 May 2019.

Vancouver:

Lin L. Roots of stochastic matrices and fractional matrix powers. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 May 21]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/roots-of-stochastic-matrices-and-fractional-matrix-powers(3f7dbb69-7c22-4fe9-9461-429c25c0db85).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528523.

Council of Science Editors:

Lin L. Roots of stochastic matrices and fractional matrix powers. [Doctoral Dissertation]. University of Manchester; 2011. Available from: https://www.research.manchester.ac.uk/portal/en/theses/roots-of-stochastic-matrices-and-fractional-matrix-powers(3f7dbb69-7c22-4fe9-9461-429c25c0db85).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528523


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 http://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 May 21, 2019. http://uknowledge.uky.edu/cs_etds/15.

MLA Handbook (7th Edition):

Thapa, Nirmal. “CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION.” 2013. Web. 21 May 2019.

Vancouver:

Thapa N. CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION. [Internet] [Doctoral dissertation]. University of Kentucky; 2013. [cited 2019 May 21]. Available from: http://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: http://uknowledge.uky.edu/cs_etds/15


University of Colorado

6. 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 http://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 May 21, 2019. http://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. 21 May 2019.

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 2019 May 21]. Available from: http://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: http://scholar.colorado.edu/appm_gradetds/68


Vanderbilt University

7. 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. (Masters Thesis). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-07172015-094412/ ;

Chicago Manual of Style (16th Edition):

Zhong, Xue. “Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping.” 2015. Masters Thesis, Vanderbilt University. Accessed May 21, 2019. http://etd.library.vanderbilt.edu/available/etd-07172015-094412/ ;.

MLA Handbook (7th Edition):

Zhong, Xue. “Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping.” 2015. Web. 21 May 2019.

Vancouver:

Zhong X. Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping. [Internet] [Masters thesis]. Vanderbilt University; 2015. [cited 2019 May 21]. Available from: http://etd.library.vanderbilt.edu/available/etd-07172015-094412/ ;.

Council of Science Editors:

Zhong X. Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping. [Masters Thesis]. Vanderbilt University; 2015. Available from: http://etd.library.vanderbilt.edu/available/etd-07172015-094412/ ;


NSYSU

8. 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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Yang K. Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 May 21]. 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


Université Catholique de Louvain

9. Gillis, Nicolas. Nonnegative matrix factorization : complexity, algorithms and applications.

Degree: 2011, Université Catholique de Louvain

Linear dimensionality reduction techniques such as principal component analysis are powerful tools for the analysis of high-dimensional data. In this thesis, we explore a closely… (more)

Subjects/Keywords: Low-rank matrix approximation; Nonnegative matrices; Computational complexity; Optimization; Underapproximation; Data mining; Hyperspectral image analysis

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APA (6th Edition):

Gillis, N. (2011). Nonnegative matrix factorization : complexity, algorithms and applications. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/70744

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):

Gillis, Nicolas. “Nonnegative matrix factorization : complexity, algorithms and applications.” 2011. Thesis, Université Catholique de Louvain. Accessed May 21, 2019. http://hdl.handle.net/2078.1/70744.

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

MLA Handbook (7th Edition):

Gillis, Nicolas. “Nonnegative matrix factorization : complexity, algorithms and applications.” 2011. Web. 21 May 2019.

Vancouver:

Gillis N. Nonnegative matrix factorization : complexity, algorithms and applications. [Internet] [Thesis]. Université Catholique de Louvain; 2011. [cited 2019 May 21]. Available from: http://hdl.handle.net/2078.1/70744.

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

Council of Science Editors:

Gillis N. Nonnegative matrix factorization : complexity, algorithms and applications. [Thesis]. Université Catholique de Louvain; 2011. Available from: http://hdl.handle.net/2078.1/70744

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

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… …nonnegative method. 1 1.2 Motivation from real data Lee and Seung popularized nonnegative matrix… …quantization (VQ), and nonnegative matrix factorization (NMF). The authors run… …quantization (VQ), and nonnegative matrix factorization (NMF). Shown in the… …By taking the magnitude of S we now have a nonnegative matrix representation of a signal… 

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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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Recoskie D. Constrained Nonnegative Matrix Factorization with Applications to Music Transcription. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2019 May 21]. 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


Georgia Tech

11. Du, Rundong. Nonnegative matrix factorization for text, graph, and hybrid data analytics.

Degree: PhD, Mathematics, 2018, Georgia Tech

 Constrained low rank approximation is a general framework for data analysis, which usually has the advantage of being simple, fast, scalable and domain general. One… (more)

Subjects/Keywords: Constrained low rank approximation; Nonnegative matrix factorization; Data analytics; Content clustering; Graph clustering

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APA (6th Edition):

Du, R. (2018). Nonnegative matrix factorization for text, graph, and hybrid data analytics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59914

Chicago Manual of Style (16th Edition):

Du, Rundong. “Nonnegative matrix factorization for text, graph, and hybrid data analytics.” 2018. Doctoral Dissertation, Georgia Tech. Accessed May 21, 2019. http://hdl.handle.net/1853/59914.

MLA Handbook (7th Edition):

Du, Rundong. “Nonnegative matrix factorization for text, graph, and hybrid data analytics.” 2018. Web. 21 May 2019.

Vancouver:

Du R. Nonnegative matrix factorization for text, graph, and hybrid data analytics. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 May 21]. Available from: http://hdl.handle.net/1853/59914.

Council of Science Editors:

Du R. Nonnegative matrix factorization for text, graph, and hybrid data analytics. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59914


University of Manchester

12. Lin, Lijing. Roots of Stochastic Matrices and Fractional Matrix Powers.

Degree: 2011, University of Manchester

 In Markov chain models in finance and healthcare a transition matrix over a certain time interval is needed but only a transition matrix over a… (more)

Subjects/Keywords: Stochastic matrix; nonnegative matrix; matrix $p$th root; primary matrix function; nonprimary matrix function; Perron – Frobenius theorem; Markov chain; transition matrix; embeddability problem; $M$-matrix; inverse eigenvalue problem; matrix power; matrix root; fractional power; Schur decomposition; Pad\'e approximation; Pad\'e approximant; matrix logarithm; matrix exponential; MATLAB

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APA (6th Edition):

Lin, L. (2011). Roots of Stochastic Matrices and Fractional Matrix Powers. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:106850

Chicago Manual of Style (16th Edition):

Lin, Lijing. “Roots of Stochastic Matrices and Fractional Matrix Powers.” 2011. Doctoral Dissertation, University of Manchester. Accessed May 21, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:106850.

MLA Handbook (7th Edition):

Lin, Lijing. “Roots of Stochastic Matrices and Fractional Matrix Powers.” 2011. Web. 21 May 2019.

Vancouver:

Lin L. Roots of Stochastic Matrices and Fractional Matrix Powers. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 May 21]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:106850.

Council of Science Editors:

Lin L. Roots of Stochastic Matrices and Fractional Matrix Powers. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:106850


North Carolina State University

13. 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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Lin MM. Inverse Problems of Matrix Data Reconstruction. [Internet] [Doctoral dissertation]. North Carolina State University; 2010. [cited 2019 May 21]. 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


Georgia State University

14. Vasireddy, Jhansi Lakshmi. Applications of Linear Algebra to Information Retrieval.

Degree: MS, Mathematics and Statistics, 2009, Georgia State University

 Some of the theory of nonnegative matrices is first presented. The Perron-Frobenius theorem is highlighted. Some of the important linear algebraic methods of information retrieval… (more)

Subjects/Keywords: PageRank; Power method; Hyper-Text Induced Topic Search; Perron-Frobenius theorem; Latent Semantic Indexing; Eigenvector; Nonnegative matrix; Eigenvalue; Mathematics

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APA (6th Edition):

Vasireddy, J. L. (2009). Applications of Linear Algebra to Information Retrieval. (Thesis). Georgia State University. Retrieved from https://scholarworks.gsu.edu/math_theses/71

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):

Vasireddy, Jhansi Lakshmi. “Applications of Linear Algebra to Information Retrieval.” 2009. Thesis, Georgia State University. Accessed May 21, 2019. https://scholarworks.gsu.edu/math_theses/71.

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

MLA Handbook (7th Edition):

Vasireddy, Jhansi Lakshmi. “Applications of Linear Algebra to Information Retrieval.” 2009. Web. 21 May 2019.

Vancouver:

Vasireddy JL. Applications of Linear Algebra to Information Retrieval. [Internet] [Thesis]. Georgia State University; 2009. [cited 2019 May 21]. Available from: https://scholarworks.gsu.edu/math_theses/71.

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

Council of Science Editors:

Vasireddy JL. Applications of Linear Algebra to Information Retrieval. [Thesis]. Georgia State University; 2009. Available from: https://scholarworks.gsu.edu/math_theses/71

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

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

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 May 21, 2019. 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. 21 May 2019.

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 2019 May 21]. 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

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

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 May 21, 2019. 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. 21 May 2019.

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 2019 May 21]. 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


NSYSU

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

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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Chou Y. Automatic Term Explanation based on Topic-regularized Recurrent Neural Network. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 May 21]. 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

18. King, Brian John. New Methods of Complex Matrix Factorization for Single-Channel Source Separation and Analysis.

Degree: PhD, 2013, University of Washington

 Throughout the day, people are constantly bombarded by a variety of sounds. Humans with normal hearing are able to easily and automatically cut through the… (more)

Subjects/Keywords: Complex matrix factorization; Digital signal processing; Nonnegative matrix factorization; Probabilistic latent component analysis; Source separation; Speech enhancement; Electrical engineering; Computer science; electrical engineering

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

APA (6th Edition):

King, B. J. (2013). New Methods of Complex Matrix Factorization for Single-Channel Source Separation and Analysis. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/22555

Chicago Manual of Style (16th Edition):

King, Brian John. “New Methods of Complex Matrix Factorization for Single-Channel Source Separation and Analysis.” 2013. Doctoral Dissertation, University of Washington. Accessed May 21, 2019. http://hdl.handle.net/1773/22555.

MLA Handbook (7th Edition):

King, Brian John. “New Methods of Complex Matrix Factorization for Single-Channel Source Separation and Analysis.” 2013. Web. 21 May 2019.

Vancouver:

King BJ. New Methods of Complex Matrix Factorization for Single-Channel Source Separation and Analysis. [Internet] [Doctoral dissertation]. University of Washington; 2013. [cited 2019 May 21]. Available from: http://hdl.handle.net/1773/22555.

Council of Science Editors:

King BJ. New Methods of Complex Matrix Factorization for Single-Channel Source Separation and Analysis. [Doctoral Dissertation]. University of Washington; 2013. Available from: http://hdl.handle.net/1773/22555

19. 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… …of the spline section satisfying (3.5) for fi . In matrix-vector form, we have… …the computational complexity. Although the large −1 matrix inversion X> X can be broken up… …methods further and incorporate them into several matrix and tensor factorization algorithms… …separation results corresponding to Figures 3.5-3.8. 28 CHAPTER 4 MATRIX AND TENSOR… 

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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 May 21, 2019. http://hdl.handle.net/2142/95277.

MLA Handbook (7th Edition):

Traa, Johannes. “Phase difference and tensor factorization models for audio source separation.” 2016. Web. 21 May 2019.

Vancouver:

Traa J. Phase difference and tensor factorization models for audio source separation. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 May 21]. 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


University of Alberta

20. Popov, Alexey. Invariant subspaces of certain classes of operators.

Degree: PhD, Department of Mathematical and Statistical Sciences, 2011, University of Alberta

 The first part of the thesis studies invariant subspaces of strictly singular operators. By a celebrated result of Aronszajn and Smith, every compact operator has… (more)

Subjects/Keywords: Invariant subspaces; Nonnegative matrices; Pure mathematics; Banach spaces; Positive operators; Matrix semigroups; Strictly singular operators; Operator theory; Functional Analysis; Almost invariant subspaces

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APA (6th Edition):

Popov, A. (2011). Invariant subspaces of certain classes of operators. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zk51vh872

Chicago Manual of Style (16th Edition):

Popov, Alexey. “Invariant subspaces of certain classes of operators.” 2011. Doctoral Dissertation, University of Alberta. Accessed May 21, 2019. https://era.library.ualberta.ca/files/zk51vh872.

MLA Handbook (7th Edition):

Popov, Alexey. “Invariant subspaces of certain classes of operators.” 2011. Web. 21 May 2019.

Vancouver:

Popov A. Invariant subspaces of certain classes of operators. [Internet] [Doctoral dissertation]. University of Alberta; 2011. [cited 2019 May 21]. Available from: https://era.library.ualberta.ca/files/zk51vh872.

Council of Science Editors:

Popov A. Invariant subspaces of certain classes of operators. [Doctoral Dissertation]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/zk51vh872

21. 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, Paris Saclay

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). Paris Saclay. 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, Paris Saclay. Accessed May 21, 2019. 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. 21 May 2019.

Vancouver:

Feng F. Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive. [Internet] [Doctoral dissertation]. Paris Saclay; 2017. [cited 2019 May 21]. 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]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLS232

22. Mandula, Ondrej. Super-resolution methods for fluorescence microscopy.

Degree: PhD, 2013, University of Edinburgh

 Fluorescence microscopy is an important tool for biological research. However, the resolution of a standard fluorescence microscope is limited by diffraction, which makes it difficult… (more)

Subjects/Keywords: 621.3; fluorescence microscopy; super-resolution; NMF; nonnegative matrix factorisation

…Application of the machine learning technique non-negative matrix factorisation (NMF)… …and contains the final summary of the thesis. Chapter 2 Non-Negative Matrix Factorisation… …for Localisation Microscopy We propose non-negative matrix factorisation (NMF) as… …with LM data containing overlapping sources. Section 2.2 introduces non-negative matrix… …the optimisation. We also show a 11 Chapter 2. Non-Negative Matrix Factorisation for… 

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APA (6th Edition):

Mandula, O. (2013). Super-resolution methods for fluorescence microscopy. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/8909

Chicago Manual of Style (16th Edition):

Mandula, Ondrej. “Super-resolution methods for fluorescence microscopy.” 2013. Doctoral Dissertation, University of Edinburgh. Accessed May 21, 2019. http://hdl.handle.net/1842/8909.

MLA Handbook (7th Edition):

Mandula, Ondrej. “Super-resolution methods for fluorescence microscopy.” 2013. Web. 21 May 2019.

Vancouver:

Mandula O. Super-resolution methods for fluorescence microscopy. [Internet] [Doctoral dissertation]. University of Edinburgh; 2013. [cited 2019 May 21]. Available from: http://hdl.handle.net/1842/8909.

Council of Science Editors:

Mandula O. Super-resolution methods for fluorescence microscopy. [Doctoral Dissertation]. University of Edinburgh; 2013. Available from: http://hdl.handle.net/1842/8909

23. 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, Paris Saclay

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). Paris Saclay. 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, Paris Saclay. Accessed May 21, 2019. 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. 21 May 2019.

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]. Paris Saclay; 2017. [cited 2019 May 21]. 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]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLS578

24. 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 (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 May 21, 2019. 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. 21 May 2019.

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 2019 May 21]. 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

25. 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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Chen BC. Robust image hash functions using higher order spectra. [Internet] [Thesis]. Queensland University of Technology; 2012. [cited 2019 May 21]. 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

26. 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 May 21, 2019. 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. 21 May 2019.

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 2019 May 21]. 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

27. 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… …nonnegativity on all the matrices. Given a matrix V with all nonnegative values, this decomposes it… …in our case. Since NMF decomposes the input nonnegative matrix V into its column… 

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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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Maddali V. Speech denoising using nonnegative matrix factorization and neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 May 21]. 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

28. 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… …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… …44 ix List of Tables 2.1 A confusion matrix applied for spike detection algorithms… …x5D;. µV denotes mean of matrix V. . . . . . . . . . . . . . . . . 25 4.1 Results of… 

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

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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Traitruengsakul S. Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms. [Internet] [Masters thesis]. Rochester Institute of Technology; 2015. [cited 2019 May 21]. 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

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

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 May 21, 2019. 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. 21 May 2019.

Vancouver:

Janecek A. Efficient feature reduction and classification methods. [Internet] [Thesis]. University of Vienna; 2009. [cited 2019 May 21]. 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

30. 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… …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… …other existing NMF algorithms and other clustering methods. 1.1 Nonnegative Matrix… 

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

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 May 21, 2019. http://hdl.handle.net/1853/52299.

MLA Handbook (7th Edition):

Kuang, Da. “Nonnegative matrix factorization for clustering.” 2014. Web. 21 May 2019.

Vancouver:

Kuang D. Nonnegative matrix factorization for clustering. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 May 21]. 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

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