KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/632528
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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.
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