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- Publisher Website: 10.1109/SAM.2010.5606734
- Scopus: eid_2-s2.0-78650096855
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Conference Paper: Robust principal component analysis? Recovering low-rank matrices from sparse errors
Title | Robust principal component analysis? Recovering low-rank matrices from sparse errors |
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Authors | |
Issue Date | 2010 |
Citation | 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010, 2010, p. 201-204 How to Cite? |
Abstract | The problem of recovering a low-rank data matrix from corrupted observations arises in many application areas, including computer vision, system identification, and bioinformatics. Recently it was shown that low-rank matrices satisfying an appropriate incoherence condition can be exactly recovered from non-vanishing fractions of errors by solving a simple convex program, Principal Component Pursuit, which minimizes a weighted combination of the nuclear norm and the ℓ1 norm of the corruption [1]. Our methodology and results suggest a principled approach to robust principal component analysis, since they show that one can efficiently and exactly recover the principal components of a low-rank data matrix even when a positive fraction of the entries are corrupted. These results extend to the case where a fraction of entries are missing as well. © 2010 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/326846 |
DC Field | Value | Language |
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dc.contributor.author | Candés, Emmanuel | - |
dc.contributor.author | Li, Xiaodong | - |
dc.contributor.author | Ma, Yi | - |
dc.contributor.author | Wright, John | - |
dc.date.accessioned | 2023-03-31T05:26:56Z | - |
dc.date.available | 2023-03-31T05:26:56Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010, 2010, p. 201-204 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326846 | - |
dc.description.abstract | The problem of recovering a low-rank data matrix from corrupted observations arises in many application areas, including computer vision, system identification, and bioinformatics. Recently it was shown that low-rank matrices satisfying an appropriate incoherence condition can be exactly recovered from non-vanishing fractions of errors by solving a simple convex program, Principal Component Pursuit, which minimizes a weighted combination of the nuclear norm and the ℓ1 norm of the corruption [1]. Our methodology and results suggest a principled approach to robust principal component analysis, since they show that one can efficiently and exactly recover the principal components of a low-rank data matrix even when a positive fraction of the entries are corrupted. These results extend to the case where a fraction of entries are missing as well. © 2010 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010 | - |
dc.title | Robust principal component analysis? Recovering low-rank matrices from sparse errors | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/SAM.2010.5606734 | - |
dc.identifier.scopus | eid_2-s2.0-78650096855 | - |
dc.identifier.spage | 201 | - |
dc.identifier.epage | 204 | - |