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dc.contributor.authorDutta, Aritra
dc.contributor.authorHanzely, Filip
dc.contributor.authorRichtarik, Peter
dc.date.accessioned2019-04-28T13:13:56Z
dc.date.available2019-04-28T13:13:56Z
dc.date.issued2019-09-13
dc.identifier.citationDutta, A., Hanzely, F., & Richtàrik, P. (2019). A Nonconvex Projection Method for Robust PCA. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 1468–1476. doi:10.1609/aaai.v33i01.33011468
dc.identifier.doi10.1609/aaai.v33i01.33011468
dc.identifier.urihttp://hdl.handle.net/10754/632528
dc.description.abstractRobust principal component analysis (RPCA) is a well-studied problem whose goal is to decompose a matrix into the sum of low-rank and sparse components. In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it. To the best of our knowledge, this is the first paper proposing a method that solves RPCA problem without considering any objective function, convex relaxation, or surrogate convex constraints. We demonstrate through extensive numerical experiments on a variety of applications, including shadow removal, background estimation, face detection, and galaxy evolution, that our approach matches and often significantly outperforms current state-of-the-art in various ways.
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)
dc.relation.urlhttps://aaai.org/ojs/index.php/AAAI/article/view/3959
dc.rightsArchived with thanks to Proceedings of the AAAI Conference on Artificial Intelligence
dc.titleA Nonconvex Projection Method for Robust PCA
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the AAAI Conference on Artificial Intelligence
dc.eprint.versionPre-print
dc.contributor.institutionMIPT
dc.identifier.arxivid1805.07962
kaust.personDutta, Aritra
kaust.personHanzely, Filip
kaust.personRichtarik, Peter
refterms.dateFOA2019-04-29T06:33:32Z


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