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    Weighted Low Rank Approximation for Background Estimation Problems

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    Type
    Conference Paper
    Authors
    Li, Xin
    Dutta, Aritra
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-01-22
    Online Publication Date
    2018-01-22
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/630413
    
    Metadata
    Show full item record
    Abstract
    Classical principal component analysis (PCA) is not robust when the data contain sparse outliers. The use of the ℓ1 norm in the Robust PCA (RPCA) method successfully eliminates this weakness of PCA in separating the sparse outliers. Here we propose a weighted low rank (WLR) method, where a simple weight is inserted inside the Frobenius norm. We demonstrate how this method tackles often computationally expensive algorithms that rely on the ℓ1 norm. As a proof of concept, we present a background estimation model based on WLR, and we compare the model with RPCA method and with other state-of-the-art algorithms used for background estimation. Our empirical validation shows that the weighted low-rank approximation we propose here can perform as well as or better than that of RPCA and other state-of-the-art algorithms.
    Citation
    Li X, Dutta A (2017) Weighted Low Rank Approximation for Background Estimation Problems. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). Available: http://dx.doi.org/10.1109/ICCVW.2017.219.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
    Conference/Event name
    16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
    DOI
    10.1109/ICCVW.2017.219
    arXiv
    1707.01753
    Additional Links
    https://ieeexplore.ieee.org/document/8265429/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCVW.2017.219
    Scopus Count
    Collections
    Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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