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    A Nonconvex Projection Method for Robust PCA

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    Type
    Poster
    Authors
    Dutta, Aritra
    Hanzely, Filip
    Richtarik, Peter
    Date
    2019-01-13
    Permanent link to this record
    http://hdl.handle.net/10754/655712
    
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    Abstract
    A Nonconvex Projection Method for Robust PCA. -Aritra Dutta, Filip Hanzely, Peter Richtárik. Visual Computing Center, KAUST, University of Edinburgh, Moscow Institute of Physics and Technology. 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.
    Conference/Event name
    WEP Library ePoster competition 2019
    Additional Links
    https://epostersonline.com/wep2019/node/10
    Collections
    WEP Library ePoster competition 2019; Posters

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