NDLI: Principal Component Pursuit with reduced linear measurements

Content Provider	IEEE Xplore Digital Library
Author	Ganesh, A. Kerui Min Wright, J. Yi Ma
Copyright Year	2012
Description	Author affiliation: Dept. of Electrical Engineering, Columbia University (Wright, J.) \|\| Dept. of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (Ganesh, A.; Kerui Min; Yi Ma)
Abstract	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 $models.^{1}$
Starting Page	1281
Ending Page	1285
File Size	134872
Page Count	5
File Format	PDF
ISBN	9781467325806
ISSN	21578117
e-ISBN	9781467325790
e-ISBN	9781467325783
DOI	10.1109/ISIT.2012.6283063
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Manganese Matrix decomposition Information theory Linear matrix inequalities Numerical models Robustness
Content Type	Text
Resource Type	Article

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