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We consider the problem of recovering a lowrank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It has recently been shown that this problem can be solved efficiently and effectively by a convex program named Principal Component Pursuit (PCP), provided that the fraction of corrupted entries and the rank of the matrix are both sufficiently small. In this paper, we extend that result to show that the same convexdoi:10.1109/isit.2010.5513538 dblp:conf/isit/GaneshWLCM10 fatcat:azjwajqqkbbn5lnj4ywepq57wq