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You searched for subject:(Low rank matrix correction). Showing records 1 – 30 of 23375 total matches.

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Delft University of Technology

1. Swart, Wouter (author). Methods for improving the computational performance of sequentially linear analsysis.

Degree: 2018, Delft University of Technology

The numerical simulation of brittle failure with nonlinear finite element analysis (NLFEA) remains a challenge due to robustness issues. These problems are attributed to the… (more)

Subjects/Keywords: Finite Element Analysis; Preconditioning; Structural analysis; Direct method; Iterative method; Low-rank matrix correction

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APA (6th Edition):

Swart, W. (. (2018). Methods for improving the computational performance of sequentially linear analsysis. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dc35a7e3-beb7-4d46-88c6-36e6f980a597

Chicago Manual of Style (16th Edition):

Swart, Wouter (author). “Methods for improving the computational performance of sequentially linear analsysis.” 2018. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:dc35a7e3-beb7-4d46-88c6-36e6f980a597.

MLA Handbook (7th Edition):

Swart, Wouter (author). “Methods for improving the computational performance of sequentially linear analsysis.” 2018. Web. 20 Jan 2021.

Vancouver:

Swart W(. Methods for improving the computational performance of sequentially linear analsysis. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:dc35a7e3-beb7-4d46-88c6-36e6f980a597.

Council of Science Editors:

Swart W(. Methods for improving the computational performance of sequentially linear analsysis. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:dc35a7e3-beb7-4d46-88c6-36e6f980a597

2. Biradar, Rakesh. Analysis and Prediction of Community Structure Using Unsupervised Learning.

Degree: MS, 2016, Worcester Polytechnic Institute

 In this thesis, we perform analysis and prediction for community structures in graphs using unsupervised learning. The methods we use require the data matrices to… (more)

Subjects/Keywords: eRPCA; Community Prediction; Low Rank; Sparse Matrix

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APA (6th Edition):

Biradar, R. (2016). Analysis and Prediction of Community Structure Using Unsupervised Learning. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-012616-134431 ; https://digitalcommons.wpi.edu/etd-theses/138

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):

Biradar, Rakesh. “Analysis and Prediction of Community Structure Using Unsupervised Learning.” 2016. Thesis, Worcester Polytechnic Institute. Accessed January 20, 2021. etd-012616-134431 ; https://digitalcommons.wpi.edu/etd-theses/138.

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

MLA Handbook (7th Edition):

Biradar, Rakesh. “Analysis and Prediction of Community Structure Using Unsupervised Learning.” 2016. Web. 20 Jan 2021.

Vancouver:

Biradar R. Analysis and Prediction of Community Structure Using Unsupervised Learning. [Internet] [Thesis]. Worcester Polytechnic Institute; 2016. [cited 2021 Jan 20]. Available from: etd-012616-134431 ; https://digitalcommons.wpi.edu/etd-theses/138.

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

Council of Science Editors:

Biradar R. Analysis and Prediction of Community Structure Using Unsupervised Learning. [Thesis]. Worcester Polytechnic Institute; 2016. Available from: etd-012616-134431 ; https://digitalcommons.wpi.edu/etd-theses/138

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


Temple University

3. Shank, Stephen David. Low-rank solution methods for large-scale linear matrix equations.

Degree: PhD, 2014, Temple University

Mathematics

We consider low-rank solution methods for certain classes of large-scale linear matrix equations. Our aim is to adapt existing low-rank solution methods based on… (more)

Subjects/Keywords: Applied mathematics;

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APA (6th Edition):

Shank, S. D. (2014). Low-rank solution methods for large-scale linear matrix equations. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,273331

Chicago Manual of Style (16th Edition):

Shank, Stephen David. “Low-rank solution methods for large-scale linear matrix equations.” 2014. Doctoral Dissertation, Temple University. Accessed January 20, 2021. http://digital.library.temple.edu/u?/p245801coll10,273331.

MLA Handbook (7th Edition):

Shank, Stephen David. “Low-rank solution methods for large-scale linear matrix equations.” 2014. Web. 20 Jan 2021.

Vancouver:

Shank SD. Low-rank solution methods for large-scale linear matrix equations. [Internet] [Doctoral dissertation]. Temple University; 2014. [cited 2021 Jan 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,273331.

Council of Science Editors:

Shank SD. Low-rank solution methods for large-scale linear matrix equations. [Doctoral Dissertation]. Temple University; 2014. Available from: http://digital.library.temple.edu/u?/p245801coll10,273331


Colorado School of Mines

4. Yang, Dehui. Structured low-rank matrix recovery via optimization methods.

Degree: PhD, Electrical Engineering, 2018, Colorado School of Mines

 From single-molecule microscopy in biology, to collaborative filtering in recommendation systems, to quantum state tomography in physics, many scientific discoveries involve solving ill-posed inverse problems,… (more)

Subjects/Keywords: matrix completion; models; super-resolution; modal analysis; low-rank; optimization

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APA (6th Edition):

Yang, D. (2018). Structured low-rank matrix recovery via optimization methods. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172154

Chicago Manual of Style (16th Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Doctoral Dissertation, Colorado School of Mines. Accessed January 20, 2021. http://hdl.handle.net/11124/172154.

MLA Handbook (7th Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Web. 20 Jan 2021.

Vancouver:

Yang D. Structured low-rank matrix recovery via optimization methods. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2018. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/11124/172154.

Council of Science Editors:

Yang D. Structured low-rank matrix recovery via optimization methods. [Doctoral Dissertation]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172154


Georgia Tech

5. Rangel Walteros, Pedro Andres. A non-asymptotic study of low-rank estimation of smooth kernels on graphs.

Degree: PhD, Mathematics, 2014, Georgia Tech

 This dissertation investigates the problem of estimating a kernel over a large graph based on a sample of noisy observations of linear measurements of the… (more)

Subjects/Keywords: Low-rank matrix completion; Kernels on graphs; High dimensional probability

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APA (6th Edition):

Rangel Walteros, P. A. (2014). A non-asymptotic study of low-rank estimation of smooth kernels on graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52988

Chicago Manual of Style (16th Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Doctoral Dissertation, Georgia Tech. Accessed January 20, 2021. http://hdl.handle.net/1853/52988.

MLA Handbook (7th Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Web. 20 Jan 2021.

Vancouver:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1853/52988.

Council of Science Editors:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52988


Georgia Tech

6. Xia, Dong. Statistical inference for large matrices.

Degree: PhD, Mathematics, 2016, Georgia Tech

 This thesis covers two topics on matrix analysis and estimation in machine learning and statistics. The first topic is about density matrix estimation with application… (more)

Subjects/Keywords: Low rank; Matrix estimation; Singular vectors; Random perturbation

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APA (6th Edition):

Xia, D. (2016). Statistical inference for large matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55632

Chicago Manual of Style (16th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Doctoral Dissertation, Georgia Tech. Accessed January 20, 2021. http://hdl.handle.net/1853/55632.

MLA Handbook (7th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Web. 20 Jan 2021.

Vancouver:

Xia D. Statistical inference for large matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1853/55632.

Council of Science Editors:

Xia D. Statistical inference for large matrices. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55632


Princeton University

7. Zhong, Yiqiao. Spectral methods and MLE: a modern statistical perspective .

Degree: PhD, 2019, Princeton University

 Modern statistical analysis often requires the integration of statistical thinking and algorithmic thinking. There are new challenges posed for classical estimation principles. Indeed, in high-dimensional… (more)

Subjects/Keywords: Eigenvectors; High dimensional statistics; Low rank matrices; Matrix perturbation; Nonconvex optimization; Random matrix theory

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APA (6th Edition):

Zhong, Y. (2019). Spectral methods and MLE: a modern statistical perspective . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01gb19f8728

Chicago Manual of Style (16th Edition):

Zhong, Yiqiao. “Spectral methods and MLE: a modern statistical perspective .” 2019. Doctoral Dissertation, Princeton University. Accessed January 20, 2021. http://arks.princeton.edu/ark:/88435/dsp01gb19f8728.

MLA Handbook (7th Edition):

Zhong, Yiqiao. “Spectral methods and MLE: a modern statistical perspective .” 2019. Web. 20 Jan 2021.

Vancouver:

Zhong Y. Spectral methods and MLE: a modern statistical perspective . [Internet] [Doctoral dissertation]. Princeton University; 2019. [cited 2021 Jan 20]. Available from: http://arks.princeton.edu/ark:/88435/dsp01gb19f8728.

Council of Science Editors:

Zhong Y. Spectral methods and MLE: a modern statistical perspective . [Doctoral Dissertation]. Princeton University; 2019. Available from: http://arks.princeton.edu/ark:/88435/dsp01gb19f8728


University of Manchester

8. Borsdorf, Ruediger. Structured Matrix Nearness Problems:Theory and Algorithms.

Degree: 2012, University of Manchester

 In many areas of science one often has a given matrix, representing forexample a measured data set and is required to find a matrix that… (more)

Subjects/Keywords: correlation matrix; factor structure; matrix embedding; Stiefel manifold; linearly structured matrix; Grassmannian manifold; low rank; optimization over manifolds; matrix nearness problems

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APA (6th Edition):

Borsdorf, R. (2012). Structured Matrix Nearness Problems:Theory and Algorithms. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:162521

Chicago Manual of Style (16th Edition):

Borsdorf, Ruediger. “Structured Matrix Nearness Problems:Theory and Algorithms.” 2012. Doctoral Dissertation, University of Manchester. Accessed January 20, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:162521.

MLA Handbook (7th Edition):

Borsdorf, Ruediger. “Structured Matrix Nearness Problems:Theory and Algorithms.” 2012. Web. 20 Jan 2021.

Vancouver:

Borsdorf R. Structured Matrix Nearness Problems:Theory and Algorithms. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2021 Jan 20]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:162521.

Council of Science Editors:

Borsdorf R. Structured Matrix Nearness Problems:Theory and Algorithms. [Doctoral Dissertation]. University of Manchester; 2012. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:162521

9. Haraldson, Joseph. Matrix Polynomials and their Lower Rank Approximations.

Degree: 2019, University of Waterloo

 This thesis is a wide ranging work on computing a “lower-rank” approximation of a matrix polynomial using second-order non-linear optimization techniques. Two notions of rank(more)

Subjects/Keywords: numerical linear algebra; optimization; matrix polynomial; eigenvalue; gcd; low rank; low rank approximation; polynomial eigenvalue; matrix pencil; smith form; kronecker form; kernel; matrix pencil; approximate gcd

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Haraldson, J. (2019). Matrix Polynomials and their Lower Rank Approximations. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14847

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):

Haraldson, Joseph. “Matrix Polynomials and their Lower Rank Approximations.” 2019. Thesis, University of Waterloo. Accessed January 20, 2021. http://hdl.handle.net/10012/14847.

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

MLA Handbook (7th Edition):

Haraldson, Joseph. “Matrix Polynomials and their Lower Rank Approximations.” 2019. Web. 20 Jan 2021.

Vancouver:

Haraldson J. Matrix Polynomials and their Lower Rank Approximations. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10012/14847.

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

Council of Science Editors:

Haraldson J. Matrix Polynomials and their Lower Rank Approximations. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14847

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


Texas A&M University

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

Degree: PhD, Statistics, 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. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/152564

Chicago Manual of Style (16th Edition):

Sun, Ranye. “Thresholding Multivariate Regression and Generalized Principal Components.” 2014. Doctoral Dissertation, Texas A&M University. Accessed January 20, 2021. http://hdl.handle.net/1969.1/152564.

MLA Handbook (7th Edition):

Sun, Ranye. “Thresholding Multivariate Regression and Generalized Principal Components.” 2014. Web. 20 Jan 2021.

Vancouver:

Sun R. Thresholding Multivariate Regression and Generalized Principal Components. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1969.1/152564.

Council of Science Editors:

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


Université Catholique de Louvain

11. 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 January 20, 2021. 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. 20 Jan 2021.

Vancouver:

Gillis N. Nonnegative matrix factorization : complexity, algorithms and applications. [Internet] [Thesis]. Université Catholique de Louvain; 2011. [cited 2021 Jan 20]. 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

12. Zhu, Ziwei. Distributed and Robust Statistical Learning .

Degree: PhD, 2018, Princeton University

 Decentralized and corrupted data are nowadays ubiquitous, which impose fundamental challenges for modern statistical analysis. Illustrative examples are massive and decentralized data produced by distributed… (more)

Subjects/Keywords: distributed learning; high-dimensional statistics; low-rank matrix recovery; principal component analysis; regression; robust statistics

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APA (6th Edition):

Zhu, Z. (2018). Distributed and Robust Statistical Learning . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

Chicago Manual of Style (16th Edition):

Zhu, Ziwei. “Distributed and Robust Statistical Learning .” 2018. Doctoral Dissertation, Princeton University. Accessed January 20, 2021. http://arks.princeton.edu/ark:/88435/dsp01d217qs22x.

MLA Handbook (7th Edition):

Zhu, Ziwei. “Distributed and Robust Statistical Learning .” 2018. Web. 20 Jan 2021.

Vancouver:

Zhu Z. Distributed and Robust Statistical Learning . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2021 Jan 20]. Available from: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x.

Council of Science Editors:

Zhu Z. Distributed and Robust Statistical Learning . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x


University of Illinois – Urbana-Champaign

13. Balasubramanian, Arvind. Applications of low-rank matrix recovery methods in computer vision.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 The ubiquitous availability of high-dimensional data such as images and videos has generated a lot of interest in high-dimensional data analysis. One of the key… (more)

Subjects/Keywords: Image Alignment; Texture Rectification; Low-Rank Matrix Recovery; Convex Optimization; Photometric Stereo; Principal Component Pursuit

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APA (6th Edition):

Balasubramanian, A. (2012). Applications of low-rank matrix recovery methods in computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/31929

Chicago Manual of Style (16th Edition):

Balasubramanian, Arvind. “Applications of low-rank matrix recovery methods in computer vision.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/31929.

MLA Handbook (7th Edition):

Balasubramanian, Arvind. “Applications of low-rank matrix recovery methods in computer vision.” 2012. Web. 20 Jan 2021.

Vancouver:

Balasubramanian A. Applications of low-rank matrix recovery methods in computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/31929.

Council of Science Editors:

Balasubramanian A. Applications of low-rank matrix recovery methods in computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/31929


Georgia Tech

14. Zhou, Fan. Statistical inference for high dimensional data with low rank structure.

Degree: PhD, Mathematics, 2018, Georgia Tech

 We study two major topics on statistical inference for high dimensional data with low rank structure occurred in many machine learning and statistics applications. The… (more)

Subjects/Keywords: Nonparametric statistics; Matrix completion; Low rank; Nuclear norm; Tensor; Singular vector perturbation

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APA (6th Edition):

Zhou, F. (2018). Statistical inference for high dimensional data with low rank structure. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60750

Chicago Manual of Style (16th Edition):

Zhou, Fan. “Statistical inference for high dimensional data with low rank structure.” 2018. Doctoral Dissertation, Georgia Tech. Accessed January 20, 2021. http://hdl.handle.net/1853/60750.

MLA Handbook (7th Edition):

Zhou, Fan. “Statistical inference for high dimensional data with low rank structure.” 2018. Web. 20 Jan 2021.

Vancouver:

Zhou F. Statistical inference for high dimensional data with low rank structure. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/1853/60750.

Council of Science Editors:

Zhou F. Statistical inference for high dimensional data with low rank structure. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60750


University of Texas – Austin

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

Degree: PhD, 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. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32832

Chicago Manual of Style (16th Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed January 20, 2021. http://hdl.handle.net/2152/32832.

MLA Handbook (7th Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 20 Jan 2021.

Vancouver:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2152/32832.

Council of Science Editors:

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


University of Pennsylvania

16. 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 January 20, 2021. 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. 20 Jan 2021.

Vancouver:

Yang D. Singular Value Decomposition for High Dimensional Data. [Internet] [Thesis]. University of Pennsylvania; 2012. [cited 2021 Jan 20]. 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


Michigan Technological University

17. Azzam, Joy. Sub-Sampled Matrix Approximations.

Degree: PhD, Department of Mathematical Sciences, 2020, Michigan Technological University

Matrix approximations are widely used to accelerate many numerical algorithms. Current methods sample row (or column) spaces to reduce their computational footprint and approximate… (more)

Subjects/Keywords: Matrix Approximation; Inverse Matrix Approximation; Low Rank Approximation; Quasi-Newton; Randomized Numerical Linear Algebra; Preconditioner; Sub-Sampled; Other Applied Mathematics

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APA (6th Edition):

Azzam, J. (2020). Sub-Sampled Matrix Approximations. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/1002

Chicago Manual of Style (16th Edition):

Azzam, Joy. “Sub-Sampled Matrix Approximations.” 2020. Doctoral Dissertation, Michigan Technological University. Accessed January 20, 2021. https://digitalcommons.mtu.edu/etdr/1002.

MLA Handbook (7th Edition):

Azzam, Joy. “Sub-Sampled Matrix Approximations.” 2020. Web. 20 Jan 2021.

Vancouver:

Azzam J. Sub-Sampled Matrix Approximations. [Internet] [Doctoral dissertation]. Michigan Technological University; 2020. [cited 2021 Jan 20]. Available from: https://digitalcommons.mtu.edu/etdr/1002.

Council of Science Editors:

Azzam J. Sub-Sampled Matrix Approximations. [Doctoral Dissertation]. Michigan Technological University; 2020. Available from: https://digitalcommons.mtu.edu/etdr/1002

18. Amadeo, Lily. Large Scale Matrix Completion and Recommender Systems.

Degree: MS, 2015, Worcester Polytechnic Institute

 "The goal of this thesis is to extend the theory and practice of matrix completion algorithms, and how they can be utilized, improved, and scaled… (more)

Subjects/Keywords: low rank matrix; robust principal component analysis; convex relaxation; principal component analysis; recommender systems; matrix completion

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APA (6th Edition):

Amadeo, L. (2015). Large Scale Matrix Completion and Recommender Systems. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-090415-162439 ; https://digitalcommons.wpi.edu/etd-theses/1021

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):

Amadeo, Lily. “Large Scale Matrix Completion and Recommender Systems.” 2015. Thesis, Worcester Polytechnic Institute. Accessed January 20, 2021. etd-090415-162439 ; https://digitalcommons.wpi.edu/etd-theses/1021.

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

MLA Handbook (7th Edition):

Amadeo, Lily. “Large Scale Matrix Completion and Recommender Systems.” 2015. Web. 20 Jan 2021.

Vancouver:

Amadeo L. Large Scale Matrix Completion and Recommender Systems. [Internet] [Thesis]. Worcester Polytechnic Institute; 2015. [cited 2021 Jan 20]. Available from: etd-090415-162439 ; https://digitalcommons.wpi.edu/etd-theses/1021.

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

Council of Science Editors:

Amadeo L. Large Scale Matrix Completion and Recommender Systems. [Thesis]. Worcester Polytechnic Institute; 2015. Available from: etd-090415-162439 ; https://digitalcommons.wpi.edu/etd-theses/1021

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


Georgia Tech

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

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 January 20, 2021. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 20 Jan 2021.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 Jan 20]. 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

20. MIAO WEIMIN. Matrix Completion Models with Fixed Basis Coefficients and Rank Regularized Problems with Hard Constraints.

Degree: 2013, National University of Singapore

Subjects/Keywords: matrix completion; rank minimization; low rank; error bound; rank consistency; semi-nuclear norm

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

APA (6th Edition):

WEIMIN, M. (2013). Matrix Completion Models with Fixed Basis Coefficients and Rank Regularized Problems with Hard Constraints. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/37889

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):

WEIMIN, MIAO. “Matrix Completion Models with Fixed Basis Coefficients and Rank Regularized Problems with Hard Constraints.” 2013. Thesis, National University of Singapore. Accessed January 20, 2021. http://scholarbank.nus.edu.sg/handle/10635/37889.

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

MLA Handbook (7th Edition):

WEIMIN, MIAO. “Matrix Completion Models with Fixed Basis Coefficients and Rank Regularized Problems with Hard Constraints.” 2013. Web. 20 Jan 2021.

Vancouver:

WEIMIN M. Matrix Completion Models with Fixed Basis Coefficients and Rank Regularized Problems with Hard Constraints. [Internet] [Thesis]. National University of Singapore; 2013. [cited 2021 Jan 20]. Available from: http://scholarbank.nus.edu.sg/handle/10635/37889.

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

Council of Science Editors:

WEIMIN M. Matrix Completion Models with Fixed Basis Coefficients and Rank Regularized Problems with Hard Constraints. [Thesis]. National University of Singapore; 2013. Available from: http://scholarbank.nus.edu.sg/handle/10635/37889

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


University of Manchester

21. Borsdorf, Ruediger. Structured matrix nearness problems : theory and algorithms.

Degree: PhD, 2012, University of Manchester

 In many areas of science one often has a given matrix, representing for example a measured data set and is required to find a matrix(more)

Subjects/Keywords: 025.04; correlation matrix; factor structure; matrix embedding; Stiefel manifold; linearly structured matrix; Grassmannian manifold; low rank; optimization over manifolds; matrix nearness problems

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

APA (6th Edition):

Borsdorf, R. (2012). Structured matrix nearness problems : theory and algorithms. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/structured-matrix-nearness-problemstheory-and-algorithms(554f944d-9a78-4b54-90c2-1ef06866c402).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554165

Chicago Manual of Style (16th Edition):

Borsdorf, Ruediger. “Structured matrix nearness problems : theory and algorithms.” 2012. Doctoral Dissertation, University of Manchester. Accessed January 20, 2021. https://www.research.manchester.ac.uk/portal/en/theses/structured-matrix-nearness-problemstheory-and-algorithms(554f944d-9a78-4b54-90c2-1ef06866c402).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554165.

MLA Handbook (7th Edition):

Borsdorf, Ruediger. “Structured matrix nearness problems : theory and algorithms.” 2012. Web. 20 Jan 2021.

Vancouver:

Borsdorf R. Structured matrix nearness problems : theory and algorithms. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2021 Jan 20]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/structured-matrix-nearness-problemstheory-and-algorithms(554f944d-9a78-4b54-90c2-1ef06866c402).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554165.

Council of Science Editors:

Borsdorf R. Structured matrix nearness problems : theory and algorithms. [Doctoral Dissertation]. University of Manchester; 2012. Available from: https://www.research.manchester.ac.uk/portal/en/theses/structured-matrix-nearness-problemstheory-and-algorithms(554f944d-9a78-4b54-90c2-1ef06866c402).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.554165


University of Michigan

22. Nayar, Himanshu. Application of Random Matrix Theory to Multimodal Fusion.

Degree: PhD, Electrical Engineering: Systems, 2017, University of Michigan

 Multimodal data fusion is an interesting problem and its applications can be seen in image processing, signal processing and machine learning. In applications where we… (more)

Subjects/Keywords: Data driven fusion; Random Matrix Theory; Factor analysis; Low rank decomposition; Clique recovery; Electrical Engineering; Engineering

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

APA (6th Edition):

Nayar, H. (2017). Application of Random Matrix Theory to Multimodal Fusion. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140962

Chicago Manual of Style (16th Edition):

Nayar, Himanshu. “Application of Random Matrix Theory to Multimodal Fusion.” 2017. Doctoral Dissertation, University of Michigan. Accessed January 20, 2021. http://hdl.handle.net/2027.42/140962.

MLA Handbook (7th Edition):

Nayar, Himanshu. “Application of Random Matrix Theory to Multimodal Fusion.” 2017. Web. 20 Jan 2021.

Vancouver:

Nayar H. Application of Random Matrix Theory to Multimodal Fusion. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2027.42/140962.

Council of Science Editors:

Nayar H. Application of Random Matrix Theory to Multimodal Fusion. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140962


Wayne State University

23. 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 January 20, 2021. https://digitalcommons.wayne.edu/oa_dissertations/583.

MLA Handbook (7th Edition):

Wang, Lijun. “Complex data analytics via sparse, low-rank matrix approximation.” 2012. Web. 20 Jan 2021.

Vancouver:

Wang L. Complex data analytics via sparse, low-rank matrix approximation. [Internet] [Doctoral dissertation]. Wayne State University; 2012. [cited 2021 Jan 20]. 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


Northeastern University

24. Shao, Ming. Efficient transfer feature learning and its applications on social media.

Degree: PhD, Department of Electrical and Computer Engineering, 2016, Northeastern University

 In the era of social media, more and more social characteristics are conveyed by multimedia, i.e., images, videos, audios, and webpages with rich media information.… (more)

Subjects/Keywords: domain adaptation; kinship verification; low-rank matrix analysis; transfer learning; Computer vision; Machine learning; Social media; Biometric identification; Mathematical models; Matrices

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

APA (6th Edition):

Shao, M. (2016). Efficient transfer feature learning and its applications on social media. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20213068

Chicago Manual of Style (16th Edition):

Shao, Ming. “Efficient transfer feature learning and its applications on social media.” 2016. Doctoral Dissertation, Northeastern University. Accessed January 20, 2021. http://hdl.handle.net/2047/D20213068.

MLA Handbook (7th Edition):

Shao, Ming. “Efficient transfer feature learning and its applications on social media.” 2016. Web. 20 Jan 2021.

Vancouver:

Shao M. Efficient transfer feature learning and its applications on social media. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2047/D20213068.

Council of Science Editors:

Shao M. Efficient transfer feature learning and its applications on social media. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20213068


University of Illinois – Chicago

25. Bhoi, Amlaan. Invariant Kernels for Few-shot Learning.

Degree: 2019, University of Illinois – Chicago

 Recent advances in few-shot learning algorithms focus on the development of meta-learning or improvements in distance-based algorithms. However, the majority of these approaches do not… (more)

Subjects/Keywords: few-shot learning; invariance learning; adversarial learning; low-rank matrix approximation; image classification; deep learning; kernel learning

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APA (6th Edition):

Bhoi, A. (2019). Invariant Kernels for Few-shot Learning. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23714

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):

Bhoi, Amlaan. “Invariant Kernels for Few-shot Learning.” 2019. Thesis, University of Illinois – Chicago. Accessed January 20, 2021. http://hdl.handle.net/10027/23714.

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

MLA Handbook (7th Edition):

Bhoi, Amlaan. “Invariant Kernels for Few-shot Learning.” 2019. Web. 20 Jan 2021.

Vancouver:

Bhoi A. Invariant Kernels for Few-shot Learning. [Internet] [Thesis]. University of Illinois – Chicago; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10027/23714.

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

Council of Science Editors:

Bhoi A. Invariant Kernels for Few-shot Learning. [Thesis]. University of Illinois – Chicago; 2019. Available from: http://hdl.handle.net/10027/23714

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


University of Iowa

26. Wang, Tianming. Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion.

Degree: PhD, Applied Mathematical and Computational Sciences, 2018, University of Iowa

  Spectrally sparse signals arise in many applications of signal processing. A spectrally sparse signal is a mixture of a few undamped or damped complex… (more)

Subjects/Keywords: low-rank Hankel matrix completion; NMR spectroscopy; projected gradient descent; Riemannian optimization; spectrally sparse signals; Applied Mathematics

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APA (6th Edition):

Wang, T. (2018). Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/6331

Chicago Manual of Style (16th Edition):

Wang, Tianming. “Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion.” 2018. Doctoral Dissertation, University of Iowa. Accessed January 20, 2021. https://ir.uiowa.edu/etd/6331.

MLA Handbook (7th Edition):

Wang, Tianming. “Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion.” 2018. Web. 20 Jan 2021.

Vancouver:

Wang T. Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion. [Internet] [Doctoral dissertation]. University of Iowa; 2018. [cited 2021 Jan 20]. Available from: https://ir.uiowa.edu/etd/6331.

Council of Science Editors:

Wang T. Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion. [Doctoral Dissertation]. University of Iowa; 2018. Available from: https://ir.uiowa.edu/etd/6331


Rice University

27. 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 January 20, 2021. 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. 20 Jan 2021.

Vancouver:

Darvish Rouhani B. A Resource-Aware Streaming-based Framework for Big Data Analysis. [Internet] [Masters thesis]. Rice University; 2015. [cited 2021 Jan 20]. 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

28. Vinyes, Marina. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.

Degree: Docteur es, Signal, Image, Automatique, 2018, Université Paris-Est

En apprentissage automatique on a pour but d'apprendre un modèle, à partir de données, qui soit capable de faire des prédictions sur des nouvelles données… (more)

Subjects/Keywords: Normes atomiques; Optimisation convexe; Parcimonie matricielle; Rang faible; Parcimonie; Atomic norms; Convex optimisation; Matrix sparsity; Low rank; Sparsity

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APA (6th Edition):

Vinyes, M. (2018). Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2018PESC1130

Chicago Manual of Style (16th Edition):

Vinyes, Marina. “Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.” 2018. Doctoral Dissertation, Université Paris-Est. Accessed January 20, 2021. http://www.theses.fr/2018PESC1130.

MLA Handbook (7th Edition):

Vinyes, Marina. “Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.” 2018. Web. 20 Jan 2021.

Vancouver:

Vinyes M. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. [Internet] [Doctoral dissertation]. Université Paris-Est; 2018. [cited 2021 Jan 20]. Available from: http://www.theses.fr/2018PESC1130.

Council of Science Editors:

Vinyes M. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. [Doctoral Dissertation]. Université Paris-Est; 2018. Available from: http://www.theses.fr/2018PESC1130


University of Lund

29. 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: Control Engineering; low-rank approximation; model reduction; non-convex optimization; Douglas-Rachford; matrix completion; overlapping norm; k-support norm; atomic norm

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

APA (6th Edition):

Grussler, C. (2017). Rank Reduction with Convex Constraints. (Doctoral Dissertation). University of Lund. Retrieved from https://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; https://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 January 20, 2021. https://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; https://portal.research.lu.se/ws/files/19595129/Thesis.pdf.

MLA Handbook (7th Edition):

Grussler, Christian. “Rank Reduction with Convex Constraints.” 2017. Web. 20 Jan 2021.

Vancouver:

Grussler C. Rank Reduction with Convex Constraints. [Internet] [Doctoral dissertation]. University of Lund; 2017. [cited 2021 Jan 20]. Available from: https://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; https://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: https://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; https://portal.research.lu.se/ws/files/19595129/Thesis.pdf


University of Lund

30. Jiang, Fangyuan. Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision.

Degree: 2015, University of Lund

 This thesis is focused on geometric computer vision problems. The first part of the thesis aims at solving one fundamental problem, namely low-rank matrix factorization.… (more)

Subjects/Keywords: Mathematics; Computer Vision and Robotics (Autonomous Systems); Geometric Computer Vision; Low-rank Matrix Factorization; Relative Pose

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

APA (6th Edition):

Jiang, F. (2015). Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision. (Doctoral Dissertation). University of Lund. Retrieved from https://lup.lub.lu.se/record/5368358 ; https://portal.research.lu.se/ws/files/5379996/5368400.pdf

Chicago Manual of Style (16th Edition):

Jiang, Fangyuan. “Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision.” 2015. Doctoral Dissertation, University of Lund. Accessed January 20, 2021. https://lup.lub.lu.se/record/5368358 ; https://portal.research.lu.se/ws/files/5379996/5368400.pdf.

MLA Handbook (7th Edition):

Jiang, Fangyuan. “Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision.” 2015. Web. 20 Jan 2021.

Vancouver:

Jiang F. Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision. [Internet] [Doctoral dissertation]. University of Lund; 2015. [cited 2021 Jan 20]. Available from: https://lup.lub.lu.se/record/5368358 ; https://portal.research.lu.se/ws/files/5379996/5368400.pdf.

Council of Science Editors:

Jiang F. Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision. [Doctoral Dissertation]. University of Lund; 2015. Available from: https://lup.lub.lu.se/record/5368358 ; https://portal.research.lu.se/ws/files/5379996/5368400.pdf

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