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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

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

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

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

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APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Mosesov, Artem. “Adaptive Non-negative Least Squares with Applications to Non-Negative Matrix Factorization.” 2014. Masters Thesis, University of Minnesota. Accessed May 21, 2019. http://hdl.handle.net/11299/173949.

MLA Handbook (7^{th} Edition):

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

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

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

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

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Lorenz, Florian M. “Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization.” 2010. Thesis, University of Illinois – Urbana-Champaign. Accessed May 21, 2019. http://hdl.handle.net/2142/16196.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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

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

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

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

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

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Feng, Boyu. “Tensor-based Hyperspectral Image Processing Methodology and its Applications in Impervious Surface and Land Cover Mapping.” 2018. Thesis, University of Western Ontario. Accessed May 21, 2019. https://ir.lib.uwo.ca/etd/5732.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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

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

URL: 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

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

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

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

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APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Thapa, Nirmal. “CONTEXT AWARE PRIVACY PRESERVING CLUSTERING AND CLASSIFICATION.” 2013. Doctoral Dissertation, University of Kentucky. Accessed May 21, 2019. http://uknowledge.uky.edu/cs_etds/15.

MLA Handbook (7^{th} 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

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

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

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

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

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APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Charles, Richard Martin. “Matrix Patch Reordering as a Strategy for Compression, Factorization, and Pattern Detection using Nonnegative Matrix Factorization Applied to Single Images.” 2015. Doctoral Dissertation, University of Colorado. Accessed May 21, 2019. http://scholar.colorado.edu/appm_gradetds/68.

MLA Handbook (7^{th} Edition):

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

URL: http://etd.library.vanderbilt.edu/available/etd-07172015-094412/ ;

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

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

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APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} 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 (7^{th} 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

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

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

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

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yang, Kai-Jhih. “Convolutional Neural Network with Multilinear Principal Component Analysis for medical image classification.” 2018. Thesis, NSYSU. Accessed May 21, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-155316.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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

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

URL: http://hdl.handle.net/2078.1/70744

►

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Degree: 2014, University of Waterloo

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

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

Subjects/Keywords: nonnegative matrix factorization; music transcription

…*matrix* factorization, but here we focus on only one: *nonnegative* *matrix*
factorization.
Why only… …*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 (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Recoskie, Daniel. “Constrained Nonnegative Matrix Factorization with Applications to Music Transcription.” 2014. Thesis, University of Waterloo. Accessed May 21, 2019. http://hdl.handle.net/10012/8639.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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

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

URL: http://hdl.handle.net/1853/59914

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:106850

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

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

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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Lin, Matthew Min-Hsiung. “Inverse Problems of Matrix Data Reconstruction.” 2010. Doctoral Dissertation, North Carolina State University. Accessed May 21, 2019. http://www.lib.ncsu.edu/resolver/1840.16/6260.

MLA Handbook (7^{th} Edition):

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

URL: https://scholarworks.gsu.edu/math_theses/71

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

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

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

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 (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Cahill, Niall M. “An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony.” 2012. Thesis, RIAN. Accessed May 21, 2019. http://eprints.maynoothuniversity.ie/3988/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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/

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

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

►

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

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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Brisebarre, Godefroy. “Détection de changements en imagerie hyperspectrale : une approche directionnelle : Change detection in hyperspectral imagery : a directional approach.” 2014. Doctoral Dissertation, Ecole centrale de Marseille. Accessed May 21, 2019. http://www.theses.fr/2014ECDM0010.

MLA Handbook (7^{th} Edition):

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

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

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

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

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Chou, Yung-Chieh. “Automatic Term Explanation based on Topic-regularized Recurrent Neural Network.” 2018. Thesis, NSYSU. Accessed May 21, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708118-120036.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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

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

URL: http://hdl.handle.net/1773/22555

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

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

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

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

…evaluated over D look directions in a *nonnegative*
*matrix* L ∈ RD×F T and assume the factorization… …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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Traa, Johannes. “Phase difference and tensor factorization models for audio source separation.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed May 21, 2019. http://hdl.handle.net/2142/95277.

MLA Handbook (7^{th} Edition):

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

URL: https://era.library.ualberta.ca/files/zk51vh872

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

►

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

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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Feng, Fangchen. “Séparation aveugle de source : de l'instantané au convolutif : Blind source separation : from instantaneous to convolutive.” 2017. Doctoral Dissertation, Paris Saclay. Accessed May 21, 2019. http://www.theses.fr/2017SACLS232.

MLA Handbook (7^{th} Edition):

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

URL: http://hdl.handle.net/1842/8909

► 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…

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

►

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

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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Mei, Jiali. “Time series recovery and prediction with regression-enhanced nonnegative matrix factorization applied to electricity consumption : Reconstitution et prédiction de séries temporelles avec la factorisation de matrice nonnégative augmentée de régression appliquée à la consommation électrique.” 2017. Doctoral Dissertation, Paris Saclay. Accessed May 21, 2019. http://www.theses.fr/2017SACLS578.

MLA Handbook (7^{th} Edition):

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

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

►

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

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

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Lefèvre, Augustin. “Dictionary learning methods for single-channel source separation : Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur.” 2012. Doctoral Dissertation, Cachan, Ecole normale supérieure. Accessed May 21, 2019. http://www.theses.fr/2012DENS0051.

MLA Handbook (7^{th} Edition):

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

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

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

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

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Chen, Brenden Chong. “Robust image hash functions using higher order spectra.” 2012. Thesis, Queensland University of Technology. Accessed May 21, 2019. https://eprints.qut.edu.au/61087/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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/

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

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

►

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

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

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Vo, Xuan Thanh. “Apprentissage avec la parcimonie et sur des données incertaines par la programmation DC et DCA : Learning with sparsity and uncertainty by Difference of Convex functions optimization.” 2015. Doctoral Dissertation, Université de Lorraine. Accessed May 21, 2019. http://www.theses.fr/2015LORR0193.

MLA Handbook (7^{th} Edition):

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

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

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

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

…denoising: *nonnegative* *matrix*
factorization and neural networks. We have shown the performance of… …CHAPTER 2
*NONNEGATIVE* *MATRIX*
FACTORIZATION
2.1 NMF INTRODUCTION
The first method we used to… …solve this problem of speech denoising is one
using *nonnegative* *matrix* factorization (NMF… …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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Maddali, Vinay. “Speech denoising using nonnegative matrix factorization and neural networks.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed May 21, 2019. http://hdl.handle.net/2142/88986.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

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

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

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

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

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

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…

Record Details Similar Records

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Traitruengsakul, Supachan. “Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms.” 2015. Masters Thesis, Rochester Institute of Technology. Accessed May 21, 2019. https://scholarworks.rit.edu/theses/8867.

MLA Handbook (7^{th} Edition):

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

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

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

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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Janecek, Andreas. “Efficient feature reduction and classification methods.” 2009. Thesis, University of Vienna. Accessed May 21, 2019. http://othes.univie.ac.at/8205/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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/.

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/

Not specified: Masters Thesis or Doctoral Dissertation

30.
Kuang, Da.
*Nonnegative**matrix* factorization for clustering.

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

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

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

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*…

Record Details Similar Records

❌

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Kuang, Da. “Nonnegative matrix factorization for clustering.” 2014. Doctoral Dissertation, Georgia Tech. Accessed May 21, 2019. http://hdl.handle.net/1853/52299.

MLA Handbook (7^{th} 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