DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Tae-Hyun | ko |
dc.contributor.author | Matsushita, Yasuyuki | ko |
dc.contributor.author | Kweon, In So | ko |
dc.contributor.author | Wipf, David | ko |
dc.date.accessioned | 2017-12-05T02:44:02Z | - |
dc.date.available | 2017-12-05T02:44:02Z | - |
dc.date.created | 2017-11-29 | - |
dc.date.created | 2017-11-29 | - |
dc.date.issued | 2016-12 | - |
dc.identifier.citation | 30th Annual Conference on Neural Information Processing Systems, NIPS 2016, pp.1398 - 1406 | - |
dc.identifier.uri | http://hdl.handle.net/10203/227754 | - |
dc.description.abstract | Commonly used in many applications, robust PCA represents an algorithmic attempt to reduce the sensitivity of classical PCA to outliers. The basic idea is to learn a decomposition of some data matrix of interest into low rank and sparse components, the latter representing unwanted outliers. Although the resulting problem is typically NP-hard, convex relaxations provide a computationally-expedient alternative with theoretical support. However, in practical regimes performance guarantees break down and a variety of non-convex alternatives, including Bayesian-inspired models, have been proposed to boost estimation quality. Unfortunately though, without additional a priori knowledge none of these methods can significantly expand the critical operational range such that exact principal subspace recovery is possible. Into this mix we propose a novel pseudo-Bayesian algorithm that explicitly compensates for design weaknesses in many existing non-convex approaches leading to state-of-the-art performance with a sound analytical foundation. | - |
dc.language | English | - |
dc.publisher | Neural Information Processing Systems Foundation | - |
dc.title | A Pseudo-Bayesian algorithm for Robust PCA | - |
dc.type | Conference | - |
dc.identifier.wosid | 000458973703071 | - |
dc.identifier.scopusid | 2-s2.0-85018878911 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1398 | - |
dc.citation.endingpage | 1406 | - |
dc.citation.publicationname | 30th Annual Conference on Neural Information Processing Systems, NIPS 2016 | - |
dc.identifier.conferencecountry | SP | - |
dc.contributor.localauthor | Oh, Tae-Hyun | - |
dc.contributor.nonIdAuthor | Matsushita, Yasuyuki | - |
dc.contributor.nonIdAuthor | Kweon, In So | - |
dc.contributor.nonIdAuthor | Wipf, David | - |
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