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You searched for subject:(Matrix completion). Showing records 1 – 30 of 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

 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 (6th 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 (16th 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 (7th 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

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


Boston University

2. Ruchansky, Natali. Matrix completion with structure.

Degree: PhD, Computer Science, 2016, Boston University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

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

MLA Handbook (7th 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

 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 (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 April 24, 2019. 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. 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

 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 (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

  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 (6th 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 (16th 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 (7th 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

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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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


Iowa State University

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

Degree: 2017, Iowa State University

 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 (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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 April 24, 2019. http://hdl.handle.net/11124/172154.

MLA Handbook (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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… 

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APA (6th 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 (16th 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 (7th 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

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

 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

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APA (6th 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 (16th 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 (7th 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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th Edition):

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

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

Council of Science Editors:

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

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


University of New South Wales

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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.

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

APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 April 24, 2019. http://hdl.handle.net/1853/60750.

MLA Handbook (7th 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

 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

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APA (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

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

APA (6th 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 (16th 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 (7th 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

  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

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

APA (6th 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 (16th 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 (7th 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

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