Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Computer Science Program
Date
2019-09-13Permanent link to this record
http://hdl.handle.net/10754/632528
Metadata
Show full item recordAbstract
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.Citation
Dutta, 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.v33i01.33011468arXiv
1805.07962Additional Links
https://aaai.org/ojs/index.php/AAAI/article/view/3959ae974a485f413a2113503eed53cd6c53
10.1609/aaai.v33i01.33011468