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    A Nonconvex Projection Method for Robust PCA

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    1805.07962v1.pdf
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    4.889Mb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Article
    Authors
    Dutta, Aritra
    Hanzely, Filip
    Richtarik, Peter cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Applied Mathematics and Computational Science Program
    Computer Science Program
    Date
    2019-09-13
    Permanent link to this record
    http://hdl.handle.net/10754/632528
    
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    Abstract
    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.v33i01.33011468
    Publisher
    Association for the Advancement of Artificial Intelligence (AAAI)
    Journal
    Proceedings of the AAAI Conference on Artificial Intelligence
    DOI
    10.1609/aaai.v33i01.33011468
    arXiv
    1805.07962
    Additional Links
    https://aaai.org/ojs/index.php/AAAI/article/view/3959
    ae974a485f413a2113503eed53cd6c53
    10.1609/aaai.v33i01.33011468
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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