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 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.

Extracted Key Phrases

Citations per Year

66 Citations

Semantic Scholar estimates that this publication has 66 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Fazel2008CompressedSA, title={Compressed Sensing and Robust Recovery of Low Rank Matrices}, author={Maryam Fazel and E . Candes and Ben Recht and Pablo A. Parrilo}, year={2008} }