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- Publisher Website: 10.1109/ISIT.2012.6283063
- Scopus: eid_2-s2.0-84867537019
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Conference Paper: Principal Component Pursuit with reduced linear measurements
Title | Principal Component Pursuit with reduced linear measurements |
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Authors | |
Issue Date | 2012 |
Citation | IEEE International Symposium on Information Theory - Proceedings, 2012, p. 1281-1285 How to Cite? |
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. © 2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/326909 |
DC Field | Value | Language |
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dc.contributor.author | Ganesh, Arvind | - |
dc.contributor.author | Min, Kerui | - |
dc.contributor.author | Wright, John | - |
dc.contributor.author | Ma, Yi | - |
dc.date.accessioned | 2023-03-31T05:27:25Z | - |
dc.date.available | 2023-03-31T05:27:25Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | IEEE International Symposium on Information Theory - Proceedings, 2012, p. 1281-1285 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326909 | - |
dc.description.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. © 2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE International Symposium on Information Theory - Proceedings | - |
dc.title | Principal Component Pursuit with reduced linear measurements | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ISIT.2012.6283063 | - |
dc.identifier.scopus | eid_2-s2.0-84867537019 | - |
dc.identifier.spage | 1281 | - |
dc.identifier.epage | 1285 | - |