Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices

TL;DR

This paper originally proposed a method for exact recovery of low-rank matrices from corrupted data using robust PCA techniques, but it has been withdrawn due to a critical error in the proof.

Contribution

The paper introduced a novel approach for robust PCA aimed at exact low-rank matrix recovery despite corruptions, which was later found to contain a significant error.

Findings

The original method claimed exact recovery under certain conditions.

The paper was withdrawn due to a critical error near equation (71).

A corrected analysis is forthcoming in a separate publication.

Abstract

This paper has been withdrawn due to a critical error near equation (71). This error causes the entire argument of the paper to collapse. Emmanuel Candes of Stanford discovered the error, and has suggested a correct analysis, which will be reported in a separate publication.