A Nonconvex Projection Method for Robust PCA

Aritra Dutta, Filip Hanzely, Peter Richtarik

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Robust 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.
Original languageEnglish (US)
Pages (from-to)1468-1476
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
StatePublished - Sep 13 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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