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65 total matches.

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Iowa State University

1.
Mao, Xiaojun.
Topics in *matrix* *completion* and genomic prediction.

Degree: 2018, Iowa State University

URL: https://lib.dr.iastate.edu/etd/16632

► This dissertation consists of three projects focused on low-rank modeling to deal with *matrix* *completion* problems and genomic prediction by adjusting spatial effects. One big…
(more)

Subjects/Keywords: Genomic Prediction; Matrix Completion; Statistics and Probability

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APA (6^{th} Edition):

Mao, X. (2018). Topics in matrix completion and genomic prediction. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16632

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

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

Mao, Xiaojun. “Topics in matrix completion and genomic prediction.” 2018. Thesis, Iowa State University. Accessed April 24, 2019. https://lib.dr.iastate.edu/etd/16632.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mao, Xiaojun. “Topics in matrix completion and genomic prediction.” 2018. Web. 24 Apr 2019.

Vancouver:

Mao X. Topics in matrix completion and genomic prediction. [Internet] [Thesis]. Iowa State University; 2018. [cited 2019 Apr 24]. Available from: https://lib.dr.iastate.edu/etd/16632.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mao X. Topics in matrix completion and genomic prediction. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16632

Not specified: Masters Thesis or Doctoral Dissertation

Boston University

2.
Ruchansky, Natali.
*Matrix**completion* with structure.

Degree: PhD, Computer Science, 2016, Boston University

URL: http://hdl.handle.net/2144/19743

► Often, data organized in *matrix* form contains missing entries. Further, such data has been observed to exhibit effective low-rank, and has led to interest in…
(more)

Subjects/Keywords: Computer science; Data mining; Matrix completion

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APA (6^{th} Edition):

Ruchansky, N. (2016). Matrix completion with structure. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/19743

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

Ruchansky, Natali. “Matrix completion with structure.” 2016. Doctoral Dissertation, Boston University. Accessed April 24, 2019. http://hdl.handle.net/2144/19743.

MLA Handbook (7^{th} Edition):

Ruchansky, Natali. “Matrix completion with structure.” 2016. Web. 24 Apr 2019.

Vancouver:

Ruchansky N. Matrix completion with structure. [Internet] [Doctoral dissertation]. Boston University; 2016. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2144/19743.

Council of Science Editors:

Ruchansky N. Matrix completion with structure. [Doctoral Dissertation]. Boston University; 2016. Available from: http://hdl.handle.net/2144/19743

University of Texas – Austin

3. Gunasekar, Suriya. Mining structured matrices in high dimensions.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/43772

► Structured matrices refer to *matrix* valued data that are embedded in an inherent lower dimensional manifold with smaller degrees of freedom compared to the ambient…
(more)

Subjects/Keywords: Matrix completion; High dimensional estimation; EHRs; Letor; Matrix estimation; Sample complexity

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APA (6^{th} Edition):

Gunasekar, S. (2016). Mining structured matrices in high dimensions. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43772

Not specified: Masters Thesis or Doctoral Dissertation

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

Gunasekar, Suriya. “Mining structured matrices in high dimensions.” 2016. Thesis, University of Texas – Austin. Accessed April 24, 2019. http://hdl.handle.net/2152/43772.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Gunasekar, Suriya. “Mining structured matrices in high dimensions.” 2016. Web. 24 Apr 2019.

Vancouver:

Gunasekar S. Mining structured matrices in high dimensions. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2152/43772.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gunasekar S. Mining structured matrices in high dimensions. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43772

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

4.
Sun, Ruoyu.
*Matrix**Completion* via Nonconvex Factorization: Algorithms and Theory.

Degree: PhD, Electrical/Computer Engineering, 2015, University of Minnesota

URL: http://hdl.handle.net/11299/175344

► Learning low-rank structure of the data *matrix* is a powerful method to deal with ``big data''. However, in many modern applications such as recommendation systems…
(more)

Subjects/Keywords: alternating minimization; matrix completion; matrix factorization; nonconvex; optimization; SGD

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APA (6^{th} Edition):

Sun, R. (2015). Matrix Completion via Nonconvex Factorization: Algorithms and Theory. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/175344

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

Sun, Ruoyu. “Matrix Completion via Nonconvex Factorization: Algorithms and Theory.” 2015. Doctoral Dissertation, University of Minnesota. Accessed April 24, 2019. http://hdl.handle.net/11299/175344.

MLA Handbook (7^{th} Edition):

Sun, Ruoyu. “Matrix Completion via Nonconvex Factorization: Algorithms and Theory.” 2015. Web. 24 Apr 2019.

Vancouver:

Sun R. Matrix Completion via Nonconvex Factorization: Algorithms and Theory. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/11299/175344.

Council of Science Editors:

Sun R. Matrix Completion via Nonconvex Factorization: Algorithms and Theory. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/175344

University of California – Irvine

5. Pezeshkpour, Pouya. Compact Factorization of Matrices Using Generalized Round-Rank.

Degree: Electrical and Computer Engineering, 2018, University of California – Irvine

URL: http://www.escholarship.org/uc/item/9x58b95k

► *Matrix* factorization is a popular machine learning technique, with applications invariety of domains, such as recommendation systems [16, 28], natural language processing[26], and computer vision…
(more)

Subjects/Keywords: Electrical engineering; Computer science; Generalized Round-Rank; Linear Rank; Matrix Completion; Matrix Factorization

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APA (6^{th} Edition):

Pezeshkpour, P. (2018). Compact Factorization of Matrices Using Generalized Round-Rank. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/9x58b95k

Not specified: Masters Thesis or Doctoral Dissertation

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

Pezeshkpour, Pouya. “Compact Factorization of Matrices Using Generalized Round-Rank.” 2018. Thesis, University of California – Irvine. Accessed April 24, 2019. http://www.escholarship.org/uc/item/9x58b95k.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Pezeshkpour, Pouya. “Compact Factorization of Matrices Using Generalized Round-Rank.” 2018. Web. 24 Apr 2019.

Vancouver:

Pezeshkpour P. Compact Factorization of Matrices Using Generalized Round-Rank. [Internet] [Thesis]. University of California – Irvine; 2018. [cited 2019 Apr 24]. Available from: http://www.escholarship.org/uc/item/9x58b95k.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pezeshkpour P. Compact Factorization of Matrices Using Generalized Round-Rank. [Thesis]. University of California – Irvine; 2018. Available from: http://www.escholarship.org/uc/item/9x58b95k

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

6. Sharma, Mohit. Preference modeling and Accuracy in Recommender Systems.

Degree: PhD, Computer Science, 2017, University of Minnesota

URL: http://hdl.handle.net/11299/192686

► Recommender systems are widely used to recommend the most appealing items to users. In this thesis, we focus on analyzing the accuracy of the state-of-the-art…
(more)

Subjects/Keywords: Cold-Start item recommendations; Collaborative filtering; Group of items; Matrix completion; Matrix factorization; Recommender systems

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APA (6^{th} Edition):

Sharma, M. (2017). Preference modeling and Accuracy in Recommender Systems. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/192686

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

Sharma, Mohit. “Preference modeling and Accuracy in Recommender Systems.” 2017. Doctoral Dissertation, University of Minnesota. Accessed April 24, 2019. http://hdl.handle.net/11299/192686.

MLA Handbook (7^{th} Edition):

Sharma, Mohit. “Preference modeling and Accuracy in Recommender Systems.” 2017. Web. 24 Apr 2019.

Vancouver:

Sharma M. Preference modeling and Accuracy in Recommender Systems. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/11299/192686.

Council of Science Editors:

Sharma M. Preference modeling and Accuracy in Recommender Systems. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/192686

University of Waterloo

7. Rahman, Adam. Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations.

Degree: 2018, University of Waterloo

URL: http://hdl.handle.net/10012/13365

► Inherent in any multivariate data is structure, which describes the general shape and distribution of the underlying point configuration. While there are potentially many types…
(more)

Subjects/Keywords: Binning; Euclidean Distance Matrix Completion; Scagnostics; Structure Retention; Statistics

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APA (6^{th} Edition):

Rahman, A. (2018). Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13365

Not specified: Masters Thesis or Doctoral Dissertation

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

Rahman, Adam. “Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations.” 2018. Thesis, University of Waterloo. Accessed April 24, 2019. http://hdl.handle.net/10012/13365.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Rahman, Adam. “Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations.” 2018. Web. 24 Apr 2019.

Vancouver:

Rahman A. Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10012/13365.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rahman A. Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13365

Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

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

Degree: PhD, Mathematics, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/52988

► 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 (6^{th} 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 (16^{th} Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/52988.

MLA Handbook (7^{th} Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Web. 24 Apr 2019.

Vancouver:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Apr 24]. 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

Carnegie Mellon University

9. Bishop, William. Combining Neural Population Recordings: Theory and Application.

Degree: 2015, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/1223

► Modern electrophysiological and optical recording techniques allow for the simultaneous monitoring of large populations of neurons. However, current technologies are still limited in the total…
(more)

Subjects/Keywords: neural recordings; stitching; electrophysiology; optical methods; dimensionality reduction; matrix completion

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APA (6^{th} Edition):

Bishop, W. (2015). Combining Neural Population Recordings: Theory and Application. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1223

Not specified: Masters Thesis or Doctoral Dissertation

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

Bishop, William. “Combining Neural Population Recordings: Theory and Application.” 2015. Thesis, Carnegie Mellon University. Accessed April 24, 2019. http://repository.cmu.edu/dissertations/1223.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bishop, William. “Combining Neural Population Recordings: Theory and Application.” 2015. Web. 24 Apr 2019.

Vancouver:

Bishop W. Combining Neural Population Recordings: Theory and Application. [Internet] [Thesis]. Carnegie Mellon University; 2015. [cited 2019 Apr 24]. Available from: http://repository.cmu.edu/dissertations/1223.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bishop W. Combining Neural Population Recordings: Theory and Application. [Thesis]. Carnegie Mellon University; 2015. Available from: http://repository.cmu.edu/dissertations/1223

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

10. Voroninski, Vladislav. PhaseLift: A Novel Methodology for Phase Retrieval.

Degree: Mathematics, 2013, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/5wq5c4bp

► In many physical settings, it is difficult or impossible to measure the phase of a signal. The problem is then to recover a signal from…
(more)

Subjects/Keywords: Mathematics; Applied mathematics; convex programming; matrix completion; phase retrieval; random matrices

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APA (6^{th} Edition):

Voroninski, V. (2013). PhaseLift: A Novel Methodology for Phase Retrieval. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/5wq5c4bp

Not specified: Masters Thesis or Doctoral Dissertation

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

Voroninski, Vladislav. “PhaseLift: A Novel Methodology for Phase Retrieval.” 2013. Thesis, University of California – Berkeley. Accessed April 24, 2019. http://www.escholarship.org/uc/item/5wq5c4bp.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Voroninski, Vladislav. “PhaseLift: A Novel Methodology for Phase Retrieval.” 2013. Web. 24 Apr 2019.

Vancouver:

Voroninski V. PhaseLift: A Novel Methodology for Phase Retrieval. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2019 Apr 24]. Available from: http://www.escholarship.org/uc/item/5wq5c4bp.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Voroninski V. PhaseLift: A Novel Methodology for Phase Retrieval. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/5wq5c4bp

Not specified: Masters Thesis or Doctoral Dissertation

University of Iowa

11. Liu, Suhui. Projected Wirtinger gradient descent for spectral compressed sensing.

Degree: PhD, Mathematics, 2017, University of Iowa

URL: https://ir.uiowa.edu/etd/5803

► In modern data and signal acquisition, one main challenge arises from the growing scale of data. The data acquisition devices, however, are often limited…
(more)

Subjects/Keywords: Matrix Completion; Projected Wirtinger Gradient Descent; Signal Reconstruction; Mathematics

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APA (6^{th} Edition):

Liu, S. (2017). Projected Wirtinger gradient descent for spectral compressed sensing. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/5803

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

Liu, Suhui. “Projected Wirtinger gradient descent for spectral compressed sensing.” 2017. Doctoral Dissertation, University of Iowa. Accessed April 24, 2019. https://ir.uiowa.edu/etd/5803.

MLA Handbook (7^{th} Edition):

Liu, Suhui. “Projected Wirtinger gradient descent for spectral compressed sensing.” 2017. Web. 24 Apr 2019.

Vancouver:

Liu S. Projected Wirtinger gradient descent for spectral compressed sensing. [Internet] [Doctoral dissertation]. University of Iowa; 2017. [cited 2019 Apr 24]. Available from: https://ir.uiowa.edu/etd/5803.

Council of Science Editors:

Liu S. Projected Wirtinger gradient descent for spectral compressed sensing. [Doctoral Dissertation]. University of Iowa; 2017. Available from: https://ir.uiowa.edu/etd/5803

Université Catholique de Louvain

12.
Cosse, Augustin.
Semidefinite programming relaxations for *matrix* *completion*, inverse scattering and blind deconvolution.

Degree: 2016, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/178083

►

The thesis studies semidefinite programming relaxations for three instances of the general affine rank minimization problem. The first instance, rank one *matrix* *completion*, was known…
(more)

Subjects/Keywords: Semidefinite programming relaxation; Lasserre hierarchy; Matrix completion; Blind deconvolution; Inverse scattering

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APA (6^{th} Edition):

Cosse, A. (2016). Semidefinite programming relaxations for matrix completion, inverse scattering and blind deconvolution. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/178083

Not specified: Masters Thesis or Doctoral Dissertation

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

Cosse, Augustin. “Semidefinite programming relaxations for matrix completion, inverse scattering and blind deconvolution.” 2016. Thesis, Université Catholique de Louvain. Accessed April 24, 2019. http://hdl.handle.net/2078.1/178083.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Cosse, Augustin. “Semidefinite programming relaxations for matrix completion, inverse scattering and blind deconvolution.” 2016. Web. 24 Apr 2019.

Vancouver:

Cosse A. Semidefinite programming relaxations for matrix completion, inverse scattering and blind deconvolution. [Internet] [Thesis]. Université Catholique de Louvain; 2016. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2078.1/178083.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cosse A. Semidefinite programming relaxations for matrix completion, inverse scattering and blind deconvolution. [Thesis]. Université Catholique de Louvain; 2016. Available from: http://hdl.handle.net/2078.1/178083

Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

13. Hubbard, Charles. Bilinear and parallel prediction methods.

Degree: 2017, Iowa State University

URL: https://lib.dr.iastate.edu/etd/16147

► To make accurate predictions about a system one must develop a model for that system. Bilinear models are often attractive options because they allow the…
(more)

Subjects/Keywords: Bilinear; GPU; matrix completion; parallel; prediction; Computer Engineering

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APA (6^{th} Edition):

Hubbard, C. (2017). Bilinear and parallel prediction methods. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16147

Not specified: Masters Thesis or Doctoral Dissertation

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

Hubbard, Charles. “Bilinear and parallel prediction methods.” 2017. Thesis, Iowa State University. Accessed April 24, 2019. https://lib.dr.iastate.edu/etd/16147.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hubbard, Charles. “Bilinear and parallel prediction methods.” 2017. Web. 24 Apr 2019.

Vancouver:

Hubbard C. Bilinear and parallel prediction methods. [Internet] [Thesis]. Iowa State University; 2017. [cited 2019 Apr 24]. Available from: https://lib.dr.iastate.edu/etd/16147.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hubbard C. Bilinear and parallel prediction methods. [Thesis]. Iowa State University; 2017. Available from: https://lib.dr.iastate.edu/etd/16147

Not specified: Masters Thesis or Doctoral Dissertation

Colorado School of Mines

14.
Yang, Dehui.
Structured low-rank *matrix* recovery via optimization methods.

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

URL: http://hdl.handle.net/11124/172154

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

APA (6^{th} 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 (16^{th} Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Doctoral Dissertation, Colorado School of Mines. Accessed April 24, 2019. http://hdl.handle.net/11124/172154.

MLA Handbook (7^{th} Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Web. 24 Apr 2019.

Vancouver:

Yang D. Structured low-rank matrix recovery via optimization methods. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2018. [cited 2019 Apr 24]. 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

15.
Cao, Yang.
Poisson *matrix* *completion* and change-point detection.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/60195

► Statistical signal processing and machine learning are very important in modern science and engineering. Many theories, methods and techniques are developed to help people extract and…
(more)

Subjects/Keywords: Matrix completion; Sequential change-point detection; Robust change detection; Online learning

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APA (6^{th} Edition):

Cao, Y. (2018). Poisson matrix completion and change-point detection. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60195

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

Cao, Yang. “Poisson matrix completion and change-point detection.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/60195.

MLA Handbook (7^{th} Edition):

Cao, Yang. “Poisson matrix completion and change-point detection.” 2018. Web. 24 Apr 2019.

Vancouver:

Cao Y. Poisson matrix completion and change-point detection. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1853/60195.

Council of Science Editors:

Cao Y. Poisson matrix completion and change-point detection. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60195

University of Minnesota

16. Soni, Akshay. Structured and Sparse Signal Estimation - Fundamental Limits and Error Bounds.

Degree: PhD, Electrical Engineering, 2015, University of Minnesota

URL: http://hdl.handle.net/11299/175341

► Over the past decade, sparsity has become one of the most prevalent themes in signal processing and Big-Data applications. In general, sparsity describes the phenomenon…
(more)

Subjects/Keywords: Compressive Sensing; Matrix Completion; Maximum Likelihood; Poisson Denoising; Structured Sparsity

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APA (6^{th} Edition):

Soni, A. (2015). Structured and Sparse Signal Estimation - Fundamental Limits and Error Bounds. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/175341

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

Soni, Akshay. “Structured and Sparse Signal Estimation - Fundamental Limits and Error Bounds.” 2015. Doctoral Dissertation, University of Minnesota. Accessed April 24, 2019. http://hdl.handle.net/11299/175341.

MLA Handbook (7^{th} Edition):

Soni, Akshay. “Structured and Sparse Signal Estimation - Fundamental Limits and Error Bounds.” 2015. Web. 24 Apr 2019.

Vancouver:

Soni A. Structured and Sparse Signal Estimation - Fundamental Limits and Error Bounds. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/11299/175341.

Council of Science Editors:

Soni A. Structured and Sparse Signal Estimation - Fundamental Limits and Error Bounds. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/175341

17. -7451-5937. Reconstructing the connectome from an ensemble of measurements.

Degree: Neuroscience, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/38169

► While connectomics paradigms have been undergoing rapid development in the experimental community, the problem of analyzing the resulting data has remained largely unaddressed. Recently, the…
(more)

Subjects/Keywords: Connectome; Matrix completion

…entries can be found. There are several alternative *matrix* *completion* algorithms such as… …low-rank *matrix* *completion*
[135], but these algorithms are either more… …139, 2011.
[21] Emmanuel J Candes and Yaniv Plan. *Matrix* *completion* with noise… …Benjamin Recht.
Exact *matrix* *completion*
via convex optimization. Foundations of Computational… …power of convex relaxation:
Near-optimal *matrix* *completion*. Information Theory, IEEE…

Record Details Similar Records

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

APA (6^{th} Edition):

-7451-5937. (2016). Reconstructing the connectome from an ensemble of measurements. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/38169

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 (16^{th} Edition):

-7451-5937. “Reconstructing the connectome from an ensemble of measurements.” 2016. Thesis, University of Texas – Austin. Accessed April 24, 2019. http://hdl.handle.net/2152/38169.

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 (7^{th} Edition):

-7451-5937. “Reconstructing the connectome from an ensemble of measurements.” 2016. Web. 24 Apr 2019.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Vancouver:

-7451-5937. Reconstructing the connectome from an ensemble of measurements. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2152/38169.

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:

-7451-5937. Reconstructing the connectome from an ensemble of measurements. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/38169

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Arizona State University

18.
Krouse, Brian Richard.
Large-Scale *Matrix* *Completion* Using Orthogonal Rank-One
*Matrix* Pursuit, Divide-Factor-Combine, and Apache Spark.

Degree: MS, Computer Science, 2014, Arizona State University

URL: http://repository.asu.edu/items/24857

► As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has…
(more)

Subjects/Keywords: Computer science; Artificial intelligence; Big Data; Hadoop; Machine Learning; Mahout; Matrix Completion; Spark

Record Details Similar Records

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

APA (6^{th} Edition):

Krouse, B. R. (2014). Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/24857

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

Krouse, Brian Richard. “Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark.” 2014. Masters Thesis, Arizona State University. Accessed April 24, 2019. http://repository.asu.edu/items/24857.

MLA Handbook (7^{th} Edition):

Krouse, Brian Richard. “Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark.” 2014. Web. 24 Apr 2019.

Vancouver:

Krouse BR. Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark. [Internet] [Masters thesis]. Arizona State University; 2014. [cited 2019 Apr 24]. Available from: http://repository.asu.edu/items/24857.

Council of Science Editors:

Krouse BR. Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark. [Masters Thesis]. Arizona State University; 2014. Available from: http://repository.asu.edu/items/24857

University of Texas – Austin

19.
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

URL: http://hdl.handle.net/2152/32832

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

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

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Thesis, University of Texas – Austin. Accessed April 24, 2019. http://hdl.handle.net/2152/32832.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 24 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 24]. Available from: http://hdl.handle.net/2152/32832.

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

Not specified: Masters Thesis or Doctoral Dissertation

University of New South Wales

20. Marjanovic, Goran. lq sparse signal estimation with applications.

Degree: Electrical Engineering & Telecommunications, 2012, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true

► The use of sparsity has emerged in the last fifteen years as an important tool for solving many problems in the areas of signal processing…
(more)

Subjects/Keywords: Inverse problems; Sparse; Non convex; Matrix completion; Inverse covariance; Linear regression; Penalized problem

Record Details Similar Records

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

APA (6^{th} Edition):

Marjanovic, G. (2012). lq sparse signal estimation with applications. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true

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

Marjanovic, Goran. “lq sparse signal estimation with applications.” 2012. Doctoral Dissertation, University of New South Wales. Accessed April 24, 2019. http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true.

MLA Handbook (7^{th} Edition):

Marjanovic, Goran. “lq sparse signal estimation with applications.” 2012. Web. 24 Apr 2019.

Vancouver:

Marjanovic G. lq sparse signal estimation with applications. [Internet] [Doctoral dissertation]. University of New South Wales; 2012. [cited 2019 Apr 24]. Available from: http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true.

Council of Science Editors:

Marjanovic G. lq sparse signal estimation with applications. [Doctoral Dissertation]. University of New South Wales; 2012. Available from: http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true

21. Zare, Armin. Low-complexity stochastic modeling of wall-bounded shear flows.

Degree: PhD, Electrical/Computer Engineering, 2016, University of Minnesota

URL: http://hdl.handle.net/11299/185120

► Turbulent flows are ubiquitous in nature and they appear in many engineering applications. Transition to turbulence, in general, increases skin-friction drag in air/water vehicles compromising…
(more)

Subjects/Keywords: Control theory; Convex optimization; Flow Control; Structured matrix completion problems; Turbulence modeling; Turbulent flows

Record Details Similar Records

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

APA (6^{th} Edition):

Zare, A. (2016). Low-complexity stochastic modeling of wall-bounded shear flows. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/185120

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

Zare, Armin. “Low-complexity stochastic modeling of wall-bounded shear flows.” 2016. Doctoral Dissertation, University of Minnesota. Accessed April 24, 2019. http://hdl.handle.net/11299/185120.

MLA Handbook (7^{th} Edition):

Zare, Armin. “Low-complexity stochastic modeling of wall-bounded shear flows.” 2016. Web. 24 Apr 2019.

Vancouver:

Zare A. Low-complexity stochastic modeling of wall-bounded shear flows. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/11299/185120.

Council of Science Editors:

Zare A. Low-complexity stochastic modeling of wall-bounded shear flows. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/185120

University of Minnesota

22. Ngo, Thanh Trung. Low dimensional approximations: problems and algorithms.

Degree: Computer Science, 2014, University of Minnesota

URL: http://hdl.handle.net/11299/163875

► High dimensional data usually have intrinsic low rank representations. These low rank representations not only reveal the hidden structure of the data but also reduce…
(more)

Subjects/Keywords: Data analysis; Dimension reduction; Low dimensional approximation; Matrix completion; Numerical linear algebra; Optimization

Record Details Similar Records

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

APA (6^{th} Edition):

Ngo, T. T. (2014). Low dimensional approximations: problems and algorithms. (Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/163875

Not specified: Masters Thesis or Doctoral Dissertation

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

Ngo, Thanh Trung. “Low dimensional approximations: problems and algorithms.” 2014. Thesis, University of Minnesota. Accessed April 24, 2019. http://hdl.handle.net/11299/163875.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ngo, Thanh Trung. “Low dimensional approximations: problems and algorithms.” 2014. Web. 24 Apr 2019.

Vancouver:

Ngo TT. Low dimensional approximations: problems and algorithms. [Internet] [Thesis]. University of Minnesota; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/11299/163875.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ngo TT. Low dimensional approximations: problems and algorithms. [Thesis]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/163875

Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

23. Chang, Yi Tan. A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis.

Degree: MS, Electrical and Computer Engineering, 2018, Virginia Tech

URL: http://hdl.handle.net/10919/83813

► This thesis consists of two projects in which various machine learning approaches and statistical analysis for the integration of biomedical data analysis were explored, developed…
(more)

Subjects/Keywords: Data integration; machine learning; pathway enrichment; pathway prioritization; matrix completion; treatment recommendation.

Record Details Similar Records

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

APA (6^{th} Edition):

Chang, Y. T. (2018). A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83813

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

Chang, Yi Tan. “A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis.” 2018. Masters Thesis, Virginia Tech. Accessed April 24, 2019. http://hdl.handle.net/10919/83813.

MLA Handbook (7^{th} Edition):

Chang, Yi Tan. “A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis.” 2018. Web. 24 Apr 2019.

Vancouver:

Chang YT. A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10919/83813.

Council of Science Editors:

Chang YT. A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83813

King Abdullah University of Science and Technology

24.
Hou, Siqing.
Neural Inductive *Matrix* *Completion* for Predicting Disease-Gene Associations.

Degree: 2018, King Abdullah University of Science and Technology

URL: http://hdl.handle.net/10754/627946

► In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side…
(more)

Subjects/Keywords: disease-gene predictions; Neural network; matrix completion; genetic disorders; human phenotype ontology

Record Details Similar Records

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

APA (6^{th} Edition):

Hou, S. (2018). Neural Inductive Matrix Completion for Predicting Disease-Gene Associations. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/627946

Not specified: Masters Thesis or Doctoral Dissertation

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

Hou, Siqing. “Neural Inductive Matrix Completion for Predicting Disease-Gene Associations.” 2018. Thesis, King Abdullah University of Science and Technology. Accessed April 24, 2019. http://hdl.handle.net/10754/627946.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hou, Siqing. “Neural Inductive Matrix Completion for Predicting Disease-Gene Associations.” 2018. Web. 24 Apr 2019.

Vancouver:

Hou S. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2018. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10754/627946.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hou S. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations. [Thesis]. King Abdullah University of Science and Technology; 2018. Available from: http://hdl.handle.net/10754/627946

Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

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

Degree: PhD, Mathematics, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/60750

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Zhou, Fan. “Statistical inference for high dimensional data with low rank structure.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 24, 2019. http://hdl.handle.net/1853/60750.

MLA Handbook (7^{th} Edition):

Zhou, Fan. “Statistical inference for high dimensional data with low rank structure.” 2018. Web. 24 Apr 2019.

Vancouver:

Zhou F. Statistical inference for high dimensional data with low rank structure. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Apr 24]. 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

EPFL

26. Thanikachalam, Niranjan. Image Based Relighting of Cultural Artifacts.

Degree: 2016, EPFL

URL: http://infoscience.epfl.ch/record/218529

► By incorporating computational methods into the image acquisition pipeline, computational photography has opened up new avenues in the representation and visualization of real world objects…
(more)

Subjects/Keywords: Inverse rendering; scene relighting; relightable photographs; stained glass windows; light transport matrix; compressive sensing; dictionary learning; matrix completion

Record Details Similar Records

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

APA (6^{th} Edition):

Thanikachalam, N. (2016). Image Based Relighting of Cultural Artifacts. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/218529

Not specified: Masters Thesis or Doctoral Dissertation

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

Thanikachalam, Niranjan. “Image Based Relighting of Cultural Artifacts.” 2016. Thesis, EPFL. Accessed April 24, 2019. http://infoscience.epfl.ch/record/218529.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Thanikachalam, Niranjan. “Image Based Relighting of Cultural Artifacts.” 2016. Web. 24 Apr 2019.

Vancouver:

Thanikachalam N. Image Based Relighting of Cultural Artifacts. [Internet] [Thesis]. EPFL; 2016. [cited 2019 Apr 24]. Available from: http://infoscience.epfl.ch/record/218529.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Thanikachalam N. Image Based Relighting of Cultural Artifacts. [Thesis]. EPFL; 2016. Available from: http://infoscience.epfl.ch/record/218529

Not specified: Masters Thesis or Doctoral Dissertation

University of Oxford

27.
Wei, Ke.
Efficient algorithms for compressed sensing and *matrix* * completion*.

Degree: PhD, 2014, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:0e2e72fb-dd0c-457b-a0a5-f91c5212f5f5 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627829

► Compressed sensing and *matrix* *completion* are two new data acquisition techniques whose efficiency is achieved by exploring low dimensional structures in high dimensional data. Despite…
(more)

Subjects/Keywords: 518; Numerical analysis; numerical algorithms; low per iteration complexity; hard thresholding; alternating minimization; compressed sensing; matrix completion

Record Details Similar Records

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

APA (6^{th} Edition):

Wei, K. (2014). Efficient algorithms for compressed sensing and matrix completion. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:0e2e72fb-dd0c-457b-a0a5-f91c5212f5f5 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627829

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

Wei, Ke. “Efficient algorithms for compressed sensing and matrix completion.” 2014. Doctoral Dissertation, University of Oxford. Accessed April 24, 2019. http://ora.ox.ac.uk/objects/uuid:0e2e72fb-dd0c-457b-a0a5-f91c5212f5f5 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627829.

MLA Handbook (7^{th} Edition):

Wei, Ke. “Efficient algorithms for compressed sensing and matrix completion.” 2014. Web. 24 Apr 2019.

Vancouver:

Wei K. Efficient algorithms for compressed sensing and matrix completion. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2019 Apr 24]. Available from: http://ora.ox.ac.uk/objects/uuid:0e2e72fb-dd0c-457b-a0a5-f91c5212f5f5 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627829.

Council of Science Editors:

Wei K. Efficient algorithms for compressed sensing and matrix completion. [Doctoral Dissertation]. University of Oxford; 2014. Available from: http://ora.ox.ac.uk/objects/uuid:0e2e72fb-dd0c-457b-a0a5-f91c5212f5f5 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627829

EPFL

28. Kyrillidis, Anastasios. Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences.

Degree: 2014, EPFL

URL: http://infoscience.epfl.ch/record/202053

► Many natural and man-made signals can be described as having a few degrees of freedom relative to their size due to natural parameterizations or constraints;…
(more)

Subjects/Keywords: Sparse Euclidean projections; sparse linear regression; compressed sensing; affine rank minimization; matrix completion; structured sparsity; convex composite minimization; self-concordance

Record Details Similar Records

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

APA (6^{th} Edition):

Kyrillidis, A. (2014). Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/202053

Not specified: Masters Thesis or Doctoral Dissertation

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

Kyrillidis, Anastasios. “Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences.” 2014. Thesis, EPFL. Accessed April 24, 2019. http://infoscience.epfl.ch/record/202053.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Kyrillidis, Anastasios. “Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences.” 2014. Web. 24 Apr 2019.

Vancouver:

Kyrillidis A. Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Apr 24]. Available from: http://infoscience.epfl.ch/record/202053.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kyrillidis A. Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/202053

Not specified: Masters Thesis or Doctoral Dissertation

Arizona State University

29.
Fan, Jie.
The Design of A *Matrix* *Completion* Signal Recovery Method for
Array Processing.

Degree: Electrical Engineering, 2016, Arizona State University

URL: http://repository.asu.edu/items/40716

Subjects/Keywords: Electrical engineering; Array Processing; Matrix Completion; Signal Recovery

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Fan, J. (2016). The Design of A Matrix Completion Signal Recovery Method for Array Processing. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/40716

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

Fan, Jie. “The Design of A Matrix Completion Signal Recovery Method for Array Processing.” 2016. Masters Thesis, Arizona State University. Accessed April 24, 2019. http://repository.asu.edu/items/40716.

MLA Handbook (7^{th} Edition):

Fan, Jie. “The Design of A Matrix Completion Signal Recovery Method for Array Processing.” 2016. Web. 24 Apr 2019.

Vancouver:

Fan J. The Design of A Matrix Completion Signal Recovery Method for Array Processing. [Internet] [Masters thesis]. Arizona State University; 2016. [cited 2019 Apr 24]. Available from: http://repository.asu.edu/items/40716.

Council of Science Editors:

Fan J. The Design of A Matrix Completion Signal Recovery Method for Array Processing. [Masters Thesis]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/40716

University of Iowa

30. Mishra, Kumar Vijay. Compressed sensing applied to weather radar.

Degree: PhD, Electrical and Computer Engineering, 2015, University of Iowa

URL: https://ir.uiowa.edu/etd/1885

► Over the last two decades, dual-polarimetric weather radar has proven to be a valuable instrument providing critical precipitation information through remote sensing of the…
(more)

Subjects/Keywords: publicabstract; compressed sensing; dual-polarization; Iowa XPOLs; matrix completion; sparsity; weather radar; Electrical and Computer Engineering

Record Details Similar Records

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

APA (6^{th} Edition):

Mishra, K. V. (2015). Compressed sensing applied to weather radar. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/1885

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

Mishra, Kumar Vijay. “Compressed sensing applied to weather radar.” 2015. Doctoral Dissertation, University of Iowa. Accessed April 24, 2019. https://ir.uiowa.edu/etd/1885.

MLA Handbook (7^{th} Edition):

Mishra, Kumar Vijay. “Compressed sensing applied to weather radar.” 2015. Web. 24 Apr 2019.

Vancouver:

Mishra KV. Compressed sensing applied to weather radar. [Internet] [Doctoral dissertation]. University of Iowa; 2015. [cited 2019 Apr 24]. Available from: https://ir.uiowa.edu/etd/1885.

Council of Science Editors:

Mishra KV. Compressed sensing applied to weather radar. [Doctoral Dissertation]. University of Iowa; 2015. Available from: https://ir.uiowa.edu/etd/1885