Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation

Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation

<p>The importance of sparse signal structures has been recognized in a plethora of applications ranging from medical imaging to group disease testing to radar technology. It has been shown in practice that various signals of interest may be (approximately) sparsely modeled, and that sparse mo...

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Bibliographic Details
Main Author: Plan, Yaniv
Format: Others
Published: 2011
Online Access:https://thesis.library.caltech.edu/6259/1/thesis.pdf
Plan, Yaniv (2011) Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/K8W9-RS71. https://resolver.caltech.edu/CaltechTHESIS:02272011-233144146 <https://resolver.caltech.edu/CaltechTHESIS:02272011-233144146>

Internet

https://thesis.library.caltech.edu/6259/1/thesis.pdf
Plan, Yaniv (2011) Compressed Sensing, Sparse Approximation, and Low-Rank Matrix Estimation. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/K8W9-RS71. https://resolver.caltech.edu/CaltechTHESIS:02272011-233144146 <https://resolver.caltech.edu/CaltechTHESIS:02272011-233144146>

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