Compressed sensing and robust recovery of low rank matrices

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...

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Bibliographic Details
Published in2008 42nd Asilomar Conference on Signals, Systems and Computers pp. 1043 - 1047
Main Authors Fazel, M., Candes, E., Recht, B., Parrilo, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Area measurement
Compressed sensing
Mathematics
Matrix decomposition
Matrix rank minimization
Noise measurement
Robustness
Signal processing
Singular value decomposition
Sparse matrices
Vectors
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Summary: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.
ISBN:9781424429400
1424429404
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.2008.5074571