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You searched for subject:(Low rank approximation). Showing records 1 – 28 of 28 total matches.

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Georgia Tech

1. Kannan, Ramakrishnan. Scalable and distributed constrained low rank approximations.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

Low rank approximation is the problem of finding two low rank factors W and H such that the rank(WH) << rank(A) and A ≈ WH.… (more)

Subjects/Keywords: Distributed; Scalable; NMF; Communication avoiding; HPC; Low rank approximation

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APA (6th Edition):

Kannan, R. (2016). Scalable and distributed constrained low rank approximations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54962

Chicago Manual of Style (16th Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2019. http://hdl.handle.net/1853/54962.

MLA Handbook (7th Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Web. 21 Apr 2019.

Vancouver:

Kannan R. Scalable and distributed constrained low rank approximations. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1853/54962.

Council of Science Editors:

Kannan R. Scalable and distributed constrained low rank approximations. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54962


University of Texas – Austin

2. -0961-6947. Seismic modeling and imaging in complex media using low-rank approximation.

Degree: Geological Sciences, 2016, University of Texas – Austin

 Seismic imaging in geologically complex areas, such as sub-salt or attenuating areas, has been one of the greatest challenges in hydrocarbon exploration. Increasing the fidelity… (more)

Subjects/Keywords: Seismic modeling; Reverse-time migration; Low-rank approximation; Seismic attenuation

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APA (6th Edition):

-0961-6947. (2016). Seismic modeling and imaging in complex media using low-rank approximation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/45954

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

Chicago Manual of Style (16th Edition):

-0961-6947. “Seismic modeling and imaging in complex media using low-rank approximation.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/45954.

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

MLA Handbook (7th Edition):

-0961-6947. “Seismic modeling and imaging in complex media using low-rank approximation.” 2016. Web. 21 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0961-6947. Seismic modeling and imaging in complex media using low-rank approximation. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/45954.

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

Council of Science Editors:

-0961-6947. Seismic modeling and imaging in complex media using low-rank approximation. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/45954

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


University of Minnesota

3. Jiang, Bo. Polynomial optimization: structures, algorithms, and engineering applications.

Degree: PhD, Industrial and Systems Engineering, 2013, University of Minnesota

 As a fundamental model in Operations Research, polynomial optimization has been receiving increasingly more attention in the recent years, due to its versatile modern applications… (more)

Subjects/Keywords: Approximation algorithms; Low-rank; Polynomial optimization; Tensor optimization

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APA (6th Edition):

Jiang, B. (2013). Polynomial optimization: structures, algorithms, and engineering applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/159747

Chicago Manual of Style (16th Edition):

Jiang, Bo. “Polynomial optimization: structures, algorithms, and engineering applications.” 2013. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://purl.umn.edu/159747.

MLA Handbook (7th Edition):

Jiang, Bo. “Polynomial optimization: structures, algorithms, and engineering applications.” 2013. Web. 21 Apr 2019.

Vancouver:

Jiang B. Polynomial optimization: structures, algorithms, and engineering applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2019 Apr 21]. Available from: http://purl.umn.edu/159747.

Council of Science Editors:

Jiang B. Polynomial optimization: structures, algorithms, and engineering applications. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/159747


Texas A&M University

4. Sun, Ranye. Thresholding Multivariate Regression and Generalized Principal Components.

Degree: 2014, Texas A&M University

 As high-dimensional data arises from various fields in science and technology, traditional multivariate methods need to be updated. Principal component analysis and reduced rank regression… (more)

Subjects/Keywords: cross-validation; iterative subspace projections; low-rank matrix approximation; regularization; transposable data.

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APA (6th Edition):

Sun, R. (2014). Thresholding Multivariate Regression and Generalized Principal Components. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/152564

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):

Sun, Ranye. “Thresholding Multivariate Regression and Generalized Principal Components.” 2014. Thesis, Texas A&M University. Accessed April 21, 2019. http://hdl.handle.net/1969.1/152564.

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

MLA Handbook (7th Edition):

Sun, Ranye. “Thresholding Multivariate Regression and Generalized Principal Components.” 2014. Web. 21 Apr 2019.

Vancouver:

Sun R. Thresholding Multivariate Regression and Generalized Principal Components. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1969.1/152564.

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

Council of Science Editors:

Sun R. Thresholding Multivariate Regression and Generalized Principal Components. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/152564

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


EPFL

5. Musharbash, Eleonora. Dynamical Low Rank approximation of PDEs with random parameters.

Degree: 2017, EPFL

 In this work, we focus on the Dynamical Low Rank (DLR) approximation of PDEs equations with random parameters. This can be interpreted as a reduced… (more)

Subjects/Keywords: Dynamical Low Rank; Dynamically Orthogonal approximation; Reduced Basismethod; Uncertainty Quantification; Navier Stokes equations; wave equations

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APA (6th Edition):

Musharbash, E. (2017). Dynamical Low Rank approximation of PDEs with random parameters. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/229419

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):

Musharbash, Eleonora. “Dynamical Low Rank approximation of PDEs with random parameters.” 2017. Thesis, EPFL. Accessed April 21, 2019. http://infoscience.epfl.ch/record/229419.

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

MLA Handbook (7th Edition):

Musharbash, Eleonora. “Dynamical Low Rank approximation of PDEs with random parameters.” 2017. Web. 21 Apr 2019.

Vancouver:

Musharbash E. Dynamical Low Rank approximation of PDEs with random parameters. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Apr 21]. Available from: http://infoscience.epfl.ch/record/229419.

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

Council of Science Editors:

Musharbash E. Dynamical Low Rank approximation of PDEs with random parameters. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/229419

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


University of California – Berkeley

6. Anderson, David Gaylord. Reliable and Efficient Algorithms for Spectrum-Revealing Low-Rank Data Analysis.

Degree: Mathematics, 2016, University of California – Berkeley

 As the amount of data collected in our world increases, reliable compression algorithms are needed when datasets become too large for practical analysis, when significant… (more)

Subjects/Keywords: Applied mathematics; Statistics; Data compression; Low-rank approximation; LU decomposition; Principal component analysis

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APA (6th Edition):

Anderson, D. G. (2016). Reliable and Efficient Algorithms for Spectrum-Revealing Low-Rank Data Analysis. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/8m20z0zv

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):

Anderson, David Gaylord. “Reliable and Efficient Algorithms for Spectrum-Revealing Low-Rank Data Analysis.” 2016. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/8m20z0zv.

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

MLA Handbook (7th Edition):

Anderson, David Gaylord. “Reliable and Efficient Algorithms for Spectrum-Revealing Low-Rank Data Analysis.” 2016. Web. 21 Apr 2019.

Vancouver:

Anderson DG. Reliable and Efficient Algorithms for Spectrum-Revealing Low-Rank Data Analysis. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/8m20z0zv.

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

Council of Science Editors:

Anderson DG. Reliable and Efficient Algorithms for Spectrum-Revealing Low-Rank Data Analysis. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/8m20z0zv

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


University of California – Berkeley

7. Duersch, Jed A. High Efficiency Spectral Analysis and BLAS-3 Randomized QRCP with Low-Rank Approximations.

Degree: Applied Mathematics, 2015, University of California – Berkeley

 The purpose of this work is to improve stability and performance of selected matrix decompositions in numerical linear algebra. Chapter 1 examines the Locally Optimal… (more)

Subjects/Keywords: Applied mathematics; eigenvalues; LOBPCG; low-rank approximation; QRCP; random sampling; spectral target

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APA (6th Edition):

Duersch, J. A. (2015). High Efficiency Spectral Analysis and BLAS-3 Randomized QRCP with Low-Rank Approximations. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9js6b6bw

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):

Duersch, Jed A. “High Efficiency Spectral Analysis and BLAS-3 Randomized QRCP with Low-Rank Approximations.” 2015. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/9js6b6bw.

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

MLA Handbook (7th Edition):

Duersch, Jed A. “High Efficiency Spectral Analysis and BLAS-3 Randomized QRCP with Low-Rank Approximations.” 2015. Web. 21 Apr 2019.

Vancouver:

Duersch JA. High Efficiency Spectral Analysis and BLAS-3 Randomized QRCP with Low-Rank Approximations. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/9js6b6bw.

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

Council of Science Editors:

Duersch JA. High Efficiency Spectral Analysis and BLAS-3 Randomized QRCP with Low-Rank Approximations. [Thesis]. University of California – Berkeley; 2015. Available from: http://www.escholarship.org/uc/item/9js6b6bw

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


Université Catholique de Louvain

8. 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 April 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 Apr 2019.

Vancouver:

Gillis N. Nonnegative matrix factorization : complexity, algorithms and applications. [Internet] [Thesis]. Université Catholique de Louvain; 2011. [cited 2019 Apr 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


University of Texas – Austin

9. Bhojanapalli, Venkata Sesha Pavana Srinadh. Large scale matrix factorization with guarantees: sampling and bi-linearity.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

Low rank matrix factorization is an important step in many high dimensional machine learning algorithms. Traditional algorithms for factorization do not scale well with the… (more)

Subjects/Keywords: Matrix completion; Non-convex optimization; Low rank approximation; Semi-definite optimization; Tensor factorization; Scalable algorithms

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APA (6th Edition):

Bhojanapalli, V. S. P. S. (2015). Large scale matrix factorization with guarantees: sampling and bi-linearity. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32832

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):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32832.

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

MLA Handbook (7th Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 21 Apr 2019.

Vancouver:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32832.

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

Council of Science Editors:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32832

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


Georgia Tech

10. 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 April 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 Apr 2019.

Vancouver:

Du R. Nonnegative matrix factorization for text, graph, and hybrid data analytics. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Apr 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 Pennsylvania

11. Yang, Dan. Singular Value Decomposition for High Dimensional Data.

Degree: 2012, University of Pennsylvania

 Singular value decomposition is a widely used tool for dimension reduction in multivariate analysis. However, when used for statistical estimation in high-dimensional low rank matrix… (more)

Subjects/Keywords: Cross validation; Denoise; Low rank matrix approximation; PCA; Penalization; Thresholding; Statistics and Probability

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APA (6th Edition):

Yang, D. (2012). Singular Value Decomposition for High Dimensional Data. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/595

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, Dan. “Singular Value Decomposition for High Dimensional Data.” 2012. Thesis, University of Pennsylvania. Accessed April 21, 2019. https://repository.upenn.edu/edissertations/595.

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

MLA Handbook (7th Edition):

Yang, Dan. “Singular Value Decomposition for High Dimensional Data.” 2012. Web. 21 Apr 2019.

Vancouver:

Yang D. Singular Value Decomposition for High Dimensional Data. [Internet] [Thesis]. University of Pennsylvania; 2012. [cited 2019 Apr 21]. Available from: https://repository.upenn.edu/edissertations/595.

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

Council of Science Editors:

Yang D. Singular Value Decomposition for High Dimensional Data. [Thesis]. University of Pennsylvania; 2012. Available from: https://repository.upenn.edu/edissertations/595

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


Boise State University

12. Wilber, Heather Denise. Numerical Computing with Functions on the Sphere and Disk.

Degree: 2016, Boise State University

 A new low rank approximation method for computing with functions in polar and spherical geometries is developed. By synthesizing a classic procedure known as the… (more)

Subjects/Keywords: guassian elimination; low rank; approximation; double Fourier sphere; Numerical Analysis and Computation

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APA (6th Edition):

Wilber, H. D. (2016). Numerical Computing with Functions on the Sphere and Disk. (Thesis). Boise State University. Retrieved from https://scholarworks.boisestate.edu/td/1158

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):

Wilber, Heather Denise. “Numerical Computing with Functions on the Sphere and Disk.” 2016. Thesis, Boise State University. Accessed April 21, 2019. https://scholarworks.boisestate.edu/td/1158.

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

MLA Handbook (7th Edition):

Wilber, Heather Denise. “Numerical Computing with Functions on the Sphere and Disk.” 2016. Web. 21 Apr 2019.

Vancouver:

Wilber HD. Numerical Computing with Functions on the Sphere and Disk. [Internet] [Thesis]. Boise State University; 2016. [cited 2019 Apr 21]. Available from: https://scholarworks.boisestate.edu/td/1158.

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

Council of Science Editors:

Wilber HD. Numerical Computing with Functions on the Sphere and Disk. [Thesis]. Boise State University; 2016. Available from: https://scholarworks.boisestate.edu/td/1158

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


Georgia Tech

13. Lee, Joonseok. Local approaches for collaborative filtering.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for… (more)

Subjects/Keywords: Recommendation systems; Collaborative filtering; Machine learning; Local low-rank assumption; Matrix factorization; Matrix approximation; Ensemble collaborative ranking

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APA (6th Edition):

Lee, J. (2015). Local approaches for collaborative filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53846

Chicago Manual of Style (16th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2019. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 21 Apr 2019.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1853/53846.

Council of Science Editors:

Lee J. Local approaches for collaborative filtering. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53846


Wayne State University

14. Wang, Lijun. Complex data analytics via sparse, low-rank matrix approximation.

Degree: PhD, Computer Science, 2012, Wayne State University

  Today, digital data is accumulated at a faster than ever speed in science, engineering, biomedicine, and real-world sensing. Data mining provides us an effective… (more)

Subjects/Keywords: abnormal event detection, Big data analytics, clustering, evolutionary clustering, large-scale data analysis, low-rank matrix approximation; Computer Sciences

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

APA (6th Edition):

Wang, L. (2012). Complex data analytics via sparse, low-rank matrix approximation. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/583

Chicago Manual of Style (16th Edition):

Wang, Lijun. “Complex data analytics via sparse, low-rank matrix approximation.” 2012. Doctoral Dissertation, Wayne State University. Accessed April 21, 2019. https://digitalcommons.wayne.edu/oa_dissertations/583.

MLA Handbook (7th Edition):

Wang, Lijun. “Complex data analytics via sparse, low-rank matrix approximation.” 2012. Web. 21 Apr 2019.

Vancouver:

Wang L. Complex data analytics via sparse, low-rank matrix approximation. [Internet] [Doctoral dissertation]. Wayne State University; 2012. [cited 2019 Apr 21]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/583.

Council of Science Editors:

Wang L. Complex data analytics via sparse, low-rank matrix approximation. [Doctoral Dissertation]. Wayne State University; 2012. Available from: https://digitalcommons.wayne.edu/oa_dissertations/583


University of Texas – Austin

15. -8687-0258. A multi-scale framework for graph based machine learning problems.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Graph data have become essential in representing and modeling relationships between entities and complex network structures in various domains such as social networks and recommender… (more)

Subjects/Keywords: Machine learning; Data mining; Spectral decomposition; Low rank approximation; Link prediction; Social network analysis; Recommender systems; Collaborative filtering

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APA (6th Edition):

-8687-0258. (2017). A multi-scale framework for graph based machine learning problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47407

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

Chicago Manual of Style (16th Edition):

-8687-0258. “A multi-scale framework for graph based machine learning problems.” 2017. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/47407.

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

MLA Handbook (7th Edition):

-8687-0258. “A multi-scale framework for graph based machine learning problems.” 2017. Web. 21 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-8687-0258. A multi-scale framework for graph based machine learning problems. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/47407.

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

Council of Science Editors:

-8687-0258. A multi-scale framework for graph based machine learning problems. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47407

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


Rice University

16. Darvish Rouhani, Bita. A Resource-Aware Streaming-based Framework for Big Data Analysis.

Degree: MS, Engineering, 2015, Rice University

 The ever growing body of digital data is challenging conventional analytical techniques in machine learning, computer vision, and signal processing. Traditional analytical methods have been… (more)

Subjects/Keywords: Streaming model; Big data; Dense matrix; Low-rank approximation; HW/SW co-design; Deep Learning; Scalable machine learning

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APA (6th Edition):

Darvish Rouhani, B. (2015). A Resource-Aware Streaming-based Framework for Big Data Analysis. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/87764

Chicago Manual of Style (16th Edition):

Darvish Rouhani, Bita. “A Resource-Aware Streaming-based Framework for Big Data Analysis.” 2015. Masters Thesis, Rice University. Accessed April 21, 2019. http://hdl.handle.net/1911/87764.

MLA Handbook (7th Edition):

Darvish Rouhani, Bita. “A Resource-Aware Streaming-based Framework for Big Data Analysis.” 2015. Web. 21 Apr 2019.

Vancouver:

Darvish Rouhani B. A Resource-Aware Streaming-based Framework for Big Data Analysis. [Internet] [Masters thesis]. Rice University; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1911/87764.

Council of Science Editors:

Darvish Rouhani B. A Resource-Aware Streaming-based Framework for Big Data Analysis. [Masters Thesis]. Rice University; 2015. Available from: http://hdl.handle.net/1911/87764


University of Lund

17. Grussler, Christian. Rank Reduction with Convex Constraints.

Degree: 2017, University of Lund

 This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as… (more)

Subjects/Keywords: Reglerteknik; low-rank approximation; model reduction; non-convex optimization; Douglas-Rachford; matrix completion; overlapping norm; k-support norm; atomic norm

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APA (6th Edition):

Grussler, C. (2017). Rank Reduction with Convex Constraints. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf

Chicago Manual of Style (16th Edition):

Grussler, Christian. “Rank Reduction with Convex Constraints.” 2017. Doctoral Dissertation, University of Lund. Accessed April 21, 2019. http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf.

MLA Handbook (7th Edition):

Grussler, Christian. “Rank Reduction with Convex Constraints.” 2017. Web. 21 Apr 2019.

Vancouver:

Grussler C. Rank Reduction with Convex Constraints. [Internet] [Doctoral dissertation]. University of Lund; 2017. [cited 2019 Apr 21]. Available from: http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf.

Council of Science Editors:

Grussler C. Rank Reduction with Convex Constraints. [Doctoral Dissertation]. University of Lund; 2017. Available from: http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf


North Carolina State University

18. 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 April 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 Apr 2019.

Vancouver:

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


EPFL

19. Kasten, Jeffrey Adam. Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure.

Degree: 2015, EPFL

 Magnetic resonance spectroscopic imaging (MRSI) is a rapidly developing medical imaging modality, capable of conferring both spatial and spectral information content, and has become a… (more)

Subjects/Keywords: Magnetic resonance spectroscopic imaging; chemical shift imaging; constrained reconstruction; low-rank approximation; non-convex optimization; total variation; MR phantoms; 3D printing; spatio-spectral modeling

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APA (6th Edition):

Kasten, J. A. (2015). Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/206244

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):

Kasten, Jeffrey Adam. “Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure.” 2015. Thesis, EPFL. Accessed April 21, 2019. http://infoscience.epfl.ch/record/206244.

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

MLA Handbook (7th Edition):

Kasten, Jeffrey Adam. “Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure.” 2015. Web. 21 Apr 2019.

Vancouver:

Kasten JA. Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure. [Internet] [Thesis]. EPFL; 2015. [cited 2019 Apr 21]. Available from: http://infoscience.epfl.ch/record/206244.

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

Council of Science Editors:

Kasten JA. Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure. [Thesis]. EPFL; 2015. Available from: http://infoscience.epfl.ch/record/206244

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


Brno University of Technology

20. Hrbáček, Radek. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci .

Degree: 2013, Brno University of Technology

 Tato práce se věnuje problematice nukleární magnetické rezonance, zejména spektroskopii a spektroskopickému zobrazování, řídké reprezentaci signálů a aproximaci s nízkou hodností. Využití spektroskopických zobrazovacích metod… (more)

Subjects/Keywords: nukleární magnetická rezonance; spektroskopie; spektroskopické zobrazování; řídká reprezentace signálů; komprimované snímání; aproximace s nízkou hodností; nuclear magnetic resonance; spectroscopy; spectroscopy imaging; sparse signal representation; compressed sensing; low-rank approximation

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APA (6th Edition):

Hrbáček, R. (2013). Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/26517

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):

Hrbáček, Radek. “Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci .” 2013. Thesis, Brno University of Technology. Accessed April 21, 2019. http://hdl.handle.net/11012/26517.

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

MLA Handbook (7th Edition):

Hrbáček, Radek. “Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci .” 2013. Web. 21 Apr 2019.

Vancouver:

Hrbáček R. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci . [Internet] [Thesis]. Brno University of Technology; 2013. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11012/26517.

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

Council of Science Editors:

Hrbáček R. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci . [Thesis]. Brno University of Technology; 2013. Available from: http://hdl.handle.net/11012/26517

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


University of Texas – Austin

21. -0613-6243. A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion.

Degree: Computational Science, Engineering, and Mathematics, 2015, University of Texas – Austin

 Quantifying uncertainties in large-scale forward and inverse PDE simulations has emerged as a central challenge facing the field of computational science and engineering. The promise… (more)

Subjects/Keywords: Bayesian inference; Infinite-dimensional inverse problems; Uncertainty quantification; Low-rank approximation; Optimality; Scalable algorithms; High performance computing; Markov chain Monte Carlo; Stochastic Newton; Seismic wave propagation

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APA (6th Edition):

-0613-6243. (2015). A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31374

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

Chicago Manual of Style (16th Edition):

-0613-6243. “A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/31374.

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

MLA Handbook (7th Edition):

-0613-6243. “A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion.” 2015. Web. 21 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0613-6243. A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/31374.

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

Council of Science Editors:

-0613-6243. A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31374

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

22. Lestandi, Lucas. Approximations de rang faible et modèles d'ordre réduit appliqués à quelques problèmes de la mécanique des fluides : Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems.

Degree: Docteur es, Mécanique, 2018, Bordeaux

Les dernières décennies ont donné lieux à d'énormes progrès dans la simulation numérique des phénomènes physiques. D'une part grâce au raffinement des méthodes de discrétisation… (more)

Subjects/Keywords: Réduction de données; Réduction de modèle; MOR; POD; Cavité entraînée; HOSVD; Tensor train; Tenseurs; Formats tensoriels; Approximation de tenseurs; Interpolation physique; Approximation de rang faible; Data reduction; Model Reduction; MOR; POD; Lid driven cavity; Low rank approximation; Tensor train; Tensors; Tensor formats; Tensor approximation; Physics interpolation; Time-scaling

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APA (6th Edition):

Lestandi, L. (2018). Approximations de rang faible et modèles d'ordre réduit appliqués à quelques problèmes de la mécanique des fluides : Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2018BORD0186

Chicago Manual of Style (16th Edition):

Lestandi, Lucas. “Approximations de rang faible et modèles d'ordre réduit appliqués à quelques problèmes de la mécanique des fluides : Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems.” 2018. Doctoral Dissertation, Bordeaux. Accessed April 21, 2019. http://www.theses.fr/2018BORD0186.

MLA Handbook (7th Edition):

Lestandi, Lucas. “Approximations de rang faible et modèles d'ordre réduit appliqués à quelques problèmes de la mécanique des fluides : Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems.” 2018. Web. 21 Apr 2019.

Vancouver:

Lestandi L. Approximations de rang faible et modèles d'ordre réduit appliqués à quelques problèmes de la mécanique des fluides : Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems. [Internet] [Doctoral dissertation]. Bordeaux; 2018. [cited 2019 Apr 21]. Available from: http://www.theses.fr/2018BORD0186.

Council of Science Editors:

Lestandi L. Approximations de rang faible et modèles d'ordre réduit appliqués à quelques problèmes de la mécanique des fluides : Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems. [Doctoral Dissertation]. Bordeaux; 2018. Available from: http://www.theses.fr/2018BORD0186

23. Nguyen, Hien M. Towards high-resolution magnetic resonance spectroscopic imaging: spatiotemporal denoising and echo-time selection.

Degree: PhD, 0115, 2012, University of Illinois – Urbana-Champaign

 Magnetic resonance spectroscopic imaging (MRSI) enables acquisition of spatial-spectral nuclear spin distributions. Compared to conventional MRI, the additional spectral information provides a powerful tool for… (more)

Subjects/Keywords: magnetic resonance (MR) spectroscopy; MR spectroscopic imaging; denoising; low-rank approximation; partially-separable functions; Cadzow enhancement.

…Lipid LORA Low-Rank Approximation LPSVD Linear Prediction and Singular Value Decomposition… …51 51 54 56 60 66 66 68 72 Chapter 4 Denoising of MRSI data with low-rank approximations… …corresponding (a) low-rank ¯ and (c) Lorentzian lineshape… …matrix H, (b) Hankel matrix H, Simulation study of low-rank filtering based on linear… …rank Llp selection based on AIC: mean AIC value (avˆ lp at (a) low noise level… 

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APA (6th Edition):

Nguyen, H. M. (2012). Towards high-resolution magnetic resonance spectroscopic imaging: spatiotemporal denoising and echo-time selection. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/29753

Chicago Manual of Style (16th Edition):

Nguyen, Hien M. “Towards high-resolution magnetic resonance spectroscopic imaging: spatiotemporal denoising and echo-time selection.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 21, 2019. http://hdl.handle.net/2142/29753.

MLA Handbook (7th Edition):

Nguyen, Hien M. “Towards high-resolution magnetic resonance spectroscopic imaging: spatiotemporal denoising and echo-time selection.” 2012. Web. 21 Apr 2019.

Vancouver:

Nguyen HM. Towards high-resolution magnetic resonance spectroscopic imaging: spatiotemporal denoising and echo-time selection. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2142/29753.

Council of Science Editors:

Nguyen HM. Towards high-resolution magnetic resonance spectroscopic imaging: spatiotemporal denoising and echo-time selection. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/29753

24. Wu, Yun-Jhong. Link Prediction and Denoising in Networks.

Degree: PhD, Statistics, 2017, University of Michigan

 Network data represent connections between units of interests, but are often noisy and/or include missing values. This thesis focuses on denoising network data via inferring… (more)

Subjects/Keywords: Network data; Link prediction; Low-rank approximation; Statistics and Numeric Data; Science

…LIST OF APPENDICES A Appendix for “Low-rank effects models for network estimation with… …estimate missing links in this scenario via subspace estimation, exploiting potential low-rank… …factorization models for network data in various contexts. Link prediction via low-rank effects models… …expressed as a low-rank effect on E[Aij | Xij ], which can characterize homophily and… …features. Exploiting the low-rank structure, we proposed a nuclear-norm regularized maximum… 

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APA (6th Edition):

Wu, Y. (2017). Link Prediction and Denoising in Networks. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138596

Chicago Manual of Style (16th Edition):

Wu, Yun-Jhong. “Link Prediction and Denoising in Networks.” 2017. Doctoral Dissertation, University of Michigan. Accessed April 21, 2019. http://hdl.handle.net/2027.42/138596.

MLA Handbook (7th Edition):

Wu, Yun-Jhong. “Link Prediction and Denoising in Networks.” 2017. Web. 21 Apr 2019.

Vancouver:

Wu Y. Link Prediction and Denoising in Networks. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2027.42/138596.

Council of Science Editors:

Wu Y. Link Prediction and Denoising in Networks. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138596


University of Oxford

25. Winkler, Anderson M. Widening the applicability of permutation inference.

Degree: PhD, 2016, University of Oxford

 This thesis is divided into three main parts. In the first, we discuss that, although permutation tests can provide exact control of false positives under… (more)

Subjects/Keywords: Brain imaging; Brain morphology; Statistics; negative binomial distribution; brain cortical volume; multi-level block permutation; brain cortical surface area; surface-based morphometry; tail approximation; permutation tests; general linear model; multiple regression; low rank matrix completion; brain cortical thickness; weak exchangeability; sib-pair design; gamma distribution; Pearson type III distribution; generalised Pareto distribution; brain morphology; non-parametric combination; repeated measurements; heritability

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APA (6th Edition):

Winkler, A. M. (2016). Widening the applicability of permutation inference. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730269

Chicago Manual of Style (16th Edition):

Winkler, Anderson M. “Widening the applicability of permutation inference.” 2016. Doctoral Dissertation, University of Oxford. Accessed April 21, 2019. https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730269.

MLA Handbook (7th Edition):

Winkler, Anderson M. “Widening the applicability of permutation inference.” 2016. Web. 21 Apr 2019.

Vancouver:

Winkler AM. Widening the applicability of permutation inference. [Internet] [Doctoral dissertation]. University of Oxford; 2016. [cited 2019 Apr 21]. Available from: https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730269.

Council of Science Editors:

Winkler AM. Widening the applicability of permutation inference. [Doctoral Dissertation]. University of Oxford; 2016. Available from: https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730269

26. Sadek, El Mostafa. Méthodes itératives pour la résolution d'équations matricielles : Iterative methods fol solving matrix equations.

Degree: Docteur es, Mathématiques appliquées et informatique. Analyse Numérique, 2015, Littoral; Université Cadi Ayyad (Marrakech, Maroc). Faculté des sciences et techniques Guéliz

Nous nous intéressons dans cette thèse, à l’étude des méthodes itératives pour la résolutiond’équations matricielles de grande taille : Lyapunov, Sylvester, Riccati et Riccatinon symétrique.L’objectif… (more)

Subjects/Keywords: Sous espaces de Krylov étendu; Méthodes blocs et globales; Approximation de rang inférieur; Equation de Lyapunov; Équation de Sylvester; Riccati continu; Riccati non symétrique; Méthode de Newton; Théorie de transport; Condition de Galerkin; Méthode de minimisation de résidu; Extended Krylov subspaces; Methods bloks and global; Low-rank approximation; Lyapunov equation; Matrix Sylvester equation; Riccati equation; Nonsymmetric Riccati equation; Newton method; Transport theory; Gelerkin condition; Minimal residual method MR

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APA (6th Edition):

Sadek, E. M. (2015). Méthodes itératives pour la résolution d'équations matricielles : Iterative methods fol solving matrix equations. (Doctoral Dissertation). Littoral; Université Cadi Ayyad (Marrakech, Maroc). Faculté des sciences et techniques Guéliz. Retrieved from http://www.theses.fr/2015DUNK0434

Chicago Manual of Style (16th Edition):

Sadek, El Mostafa. “Méthodes itératives pour la résolution d'équations matricielles : Iterative methods fol solving matrix equations.” 2015. Doctoral Dissertation, Littoral; Université Cadi Ayyad (Marrakech, Maroc). Faculté des sciences et techniques Guéliz. Accessed April 21, 2019. http://www.theses.fr/2015DUNK0434.

MLA Handbook (7th Edition):

Sadek, El Mostafa. “Méthodes itératives pour la résolution d'équations matricielles : Iterative methods fol solving matrix equations.” 2015. Web. 21 Apr 2019.

Vancouver:

Sadek EM. Méthodes itératives pour la résolution d'équations matricielles : Iterative methods fol solving matrix equations. [Internet] [Doctoral dissertation]. Littoral; Université Cadi Ayyad (Marrakech, Maroc). Faculté des sciences et techniques Guéliz; 2015. [cited 2019 Apr 21]. Available from: http://www.theses.fr/2015DUNK0434.

Council of Science Editors:

Sadek EM. Méthodes itératives pour la résolution d'équations matricielles : Iterative methods fol solving matrix equations. [Doctoral Dissertation]. Littoral; Université Cadi Ayyad (Marrakech, Maroc). Faculté des sciences et techniques Guéliz; 2015. Available from: http://www.theses.fr/2015DUNK0434

27. Kim, Jingu. Nonnegative matrix and tensor factorizations, least squares problems, and applications.

Degree: PhD, Computing, 2011, Georgia Tech

 Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data… (more)

Subjects/Keywords: Linear complementarity problem; Parallel factorization; Canonical decomposition; Active set method; Rank deficiency; l1-regularized linear regression; Mixed-norm regularization; Low rank approximation; Block principal pivoting; Nonnegativity constrained least squares; Computer science; Matrices; Least squares

…a nonnegative value, and it provides a low-rank approximation formed by factors whose… …applications: For example, the low-rank approximation of term-document matrices based on SVD has been… …elements are also nonnegative. The nonnegativity constraints imposed on the low-rank factors not… …standard factorization methods that have played a pivotal role. Low-rank approximations based on… …inherently nonnegative, and it seek low-rank factor matrices that are constrained to have only… 

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APA (6th Edition):

Kim, J. (2011). Nonnegative matrix and tensor factorizations, least squares problems, and applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42909

Chicago Manual of Style (16th Edition):

Kim, Jingu. “Nonnegative matrix and tensor factorizations, least squares problems, and applications.” 2011. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2019. http://hdl.handle.net/1853/42909.

MLA Handbook (7th Edition):

Kim, Jingu. “Nonnegative matrix and tensor factorizations, least squares problems, and applications.” 2011. Web. 21 Apr 2019.

Vancouver:

Kim J. Nonnegative matrix and tensor factorizations, least squares problems, and applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1853/42909.

Council of Science Editors:

Kim J. Nonnegative matrix and tensor factorizations, least squares problems, and applications. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42909

28. Jiang, Peng. Pattern extraction and clustering for high-dimensional discrete data.

Degree: PhD, 0112, 2014, University of Illinois – Urbana-Champaign

 We explore connections of low-rank matrix factorizations with interesting problems in data mining and machine learning. We propose a framework for solving several low-rank matrix… (more)

Subjects/Keywords: low-rank matrix factorization; binary matrix factorization; k-means clustering; approximation algorithm; pattern extraction; association rule mining; document clustering; weighted binary matrix factorization; bicluster discovery; densest k-subgraph; social network mining

…con- strained low-rank matrix approximation can be formulated as 2 F min G − UW s.t. U… …approximate a binary dataset by another one with low rank. It finds a rank-one approximation to the… …rank matrix factorization to approximate the original matrix by a product of two low-rank… …a matrix Gk that minimizes G − Gk over all matrices Gk of rank k. Low-rank matrix… …proposed by us. BMF aims to approximate a binary matrix by a product of two low-rank binary… 

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

APA (6th Edition):

Jiang, P. (2014). Pattern extraction and clustering for high-dimensional discrete data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46604

Chicago Manual of Style (16th Edition):

Jiang, Peng. “Pattern extraction and clustering for high-dimensional discrete data.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 21, 2019. http://hdl.handle.net/2142/46604.

MLA Handbook (7th Edition):

Jiang, Peng. “Pattern extraction and clustering for high-dimensional discrete data.” 2014. Web. 21 Apr 2019.

Vancouver:

Jiang P. Pattern extraction and clustering for high-dimensional discrete data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2142/46604.

Council of Science Editors:

Jiang P. Pattern extraction and clustering for high-dimensional discrete data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46604

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