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Strongly Convex Programming for Principal Component Pursuit (1209.4405v1)

Published 20 Sep 2012 in cs.IT, math.IT, and math.NA

Abstract: In this paper, we address strongly convex programming for princi- pal component pursuit with reduced linear measurements, which decomposes a superposition of a low-rank matrix and a sparse matrix from a small set of linear measurements. We first provide sufficient conditions under which the strongly convex models lead to the exact low-rank and sparse matrix recov- ery; Second, we also give suggestions on how to choose suitable parameters in practical algorithms.

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