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Low-Rank Matrix Recovery From Errors and Erasures

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4 Author(s)
Yudong Chen ; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA ; Ali Jalali ; Sujay Sanghavi ; Constantine Caramanis

This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both 1) erasures, most entries are not observed, and 2) errors, values at a constant fraction of (unknown) locations are arbitrarily corrupted. We provide a new unified performance guarantee on when minimizing nuclear norm plus l1 norm succeeds in exact recovery. Our result allows for the simultaneous presence of random and deterministic components in both the error and erasure patterns. By specializing this one single result in different ways, we recover (up to poly-log factors) as corollaries all the existing results in exact matrix completion, and exact sparse and low-rank matrix decomposition. Our unified result also provides the first guarantees for 1) recovery when we observe a vanishing fraction of entries of a corrupted matrix, and 2) deterministic matrix completion.

Published in:

IEEE Transactions on Information Theory  (Volume:59 ,  Issue: 7 )