Non-convex Robust PCA
share
We propose a new method for robust PCA -- the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support. Our method involves alternating between projecting appropriate residuals onto the set of low-rank matrices, and the set of sparse matrices; each projection is non-convex but easy to compute. In spite of this non-convexity, we establish exact recovery of the low-rank matrix, under the same conditions that are required by existing methods (which are based on convex optimization). For an m × n input matrix (m ≤ n), our method has a running time of O(r^2mn) per iteration, and needs O((1/ϵ)) iterations to reach an accuracy of ϵ. This is close to the running time of simple PCA via the power method, which requires O(rmn) per iteration, and O((1/ϵ)) iterations. In contrast, existing methods for robust PCA, which are based on convex optimization, have O(m^2n) complexity per iteration, and take O(1/ϵ) iterations, i.e., exponentially more iterations for the same accuracy. Experiments on both synthetic and real data establishes the improved speed and accuracy of our method over existing convex implementations.
READ FULL TEXT
