Principal Component Pursuit with Reduced Linear Measurements

Details Principal Component Pursuit with Reduced Linear Measurements Principal Component Pursuit with Reduced Linear Measurements

2012-02-29

arxiv.org/abs/1202.6445v1

Computer Science

Information Theory

32 pages, preliminary version submitted to ISIT'12

Scientific paper

In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data processing tasks such as aligning multiple images or rectifying regular texture, where the goal is to recover a low-rank matrix with a large fraction of corrupted entries in the presence of nonlinear domain transformation. We consider a natural convex heuristic to this problem which is a variant to the recently proposed Principal Component Pursuit. We prove that under suitable conditions, this convex program guarantees to recover the correct low-rank and sparse components despite reduced measurements. Our analysis covers both random and deterministic measurement models.

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