## Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization (2009)

Venue: | Advances in Neural Information Processing Systems 22 |

Citations: | 44 - 3 self |

### BibTeX

@INPROCEEDINGS{Wright09robustprincipal,

author = {John Wright},

title = {Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization},

booktitle = {Advances in Neural Information Processing Systems 22},

year = {2009}

}

### OpenURL

### Abstract

The supplementary material to the NIPS version of this paper [4] contains a critical error, which was discovered several days before the conference. Unfortunately, it was too late to withdraw the paper from the proceedings. Fortunately, since that time, a correct analysis of the proposed convex programming relaxation has been developed by Emmanuel Candes of Stanford University. That analysis is reported in a joint paper, Robust Principal Component Analysis? by Emmanuel Candes, Xiaodong Li, Yi Ma and John Wright,