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Compressed sensing and robust recovery of low rank matrices

Fazel, M. and Candès, E. and Recht, B. and Parrilo, P. (2008) Compressed sensing and robust recovery of low rank matrices. In: 42nd Asilomar Conference on Signals, Systems and Computers. IEEE , Piscataway, NJ, pp. 1043-1047. ISBN 978-1-4244-2940-0. https://resolver.caltech.edu/CaltechAUTHORS:20170411-162244274

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Abstract

In this paper, we focus on compressed sensing and recovery schemes for low-rank matrices, asking under what conditions a low-rank matrix can be sensed and recovered from incomplete, inaccurate, and noisy observations. We consider three schemes, one based on a certain Restricted Isometry Property and two based on directly sensing the row and column space of the matrix. We study their properties in terms of exact recovery in the ideal case, and robustness issues for approximately low-rank matrices and for noisy measurements.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/ACSSC.2008.5074571DOIArticle
http://ieeexplore.ieee.org/document/5074571/PublisherArticle
ORCID:
AuthorORCID
Candès, E.0000-0001-9234-924X
Parrilo, P.0000-0003-1132-8477
Additional Information:© 2008 IEEE.
Subject Keywords:Matrix rank minimization; compressed sensing; singular value decomposition
DOI:10.1109/ACSSC.2008.5074571
Record Number:CaltechAUTHORS:20170411-162244274
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170411-162244274
Official Citation:M. Fazel, E. Candes, B. Recht and P. Parrilo, "Compressed sensing and robust recovery of low rank matrices," 2008 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2008, pp. 1043-1047. doi: 10.1109/ACSSC.2008.5074571
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:76524
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:12 Apr 2017 15:59
Last Modified:15 Nov 2021 17:00

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