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 {\\em 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\u00d7n input matrix (m\u2264n), our method has a running time of O(r2mn) per iteration, and needs O(log(1\/\u03f5)) iterations to reach an accuracy of \u03f5. This is close to the running time of simple PCA via the power method, which requires O(rmn) per iteration, and O(log(1\/\u03f5)) iterations. In contrast, existing methods for robust PCA, which are based on convex optimization, have O(m2n) complexity per iteration, and take O(1\/\u03f5) iterations, i.e., exponentially more iterations for the same accuracy.\u00a0 Experiments on both synthetic and real data establishes the improved speed and accuracy of our method over existing convex implementations.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 {\\em non-convex} but easy to compute. 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