Fast kernel sparse representation

Hanxi Li*, Yongsheng Gao, Jun Sun

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    17 Citations (Scopus)

    Abstract

    Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios. Furthermore, inspired by the success of group-sparsity, we enforce a rigid group-sparsity constraint on KOMP which leads to a noniterative variation. The constrained cousin of KOMP, dubbed as Single-Step KOMP (S-KOMP), merely takes one step to achieve the sparse coefficients. A remarkable improvement (up to 2,750 times) in efficiency is reported for S-KOMP, with only a negligible loss of accuracy.

    Original languageEnglish
    Title of host publicationProceedings - 2011 International Conference on Digital Image Computing
    Subtitle of host publicationTechniques and Applications, DICTA 2011
    Pages72-77
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    Event2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 - Noosa, QLD, Australia
    Duration: 6 Dec 20118 Dec 2011

    Publication series

    NameProceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

    Conference

    Conference2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
    Country/TerritoryAustralia
    CityNoosa, QLD
    Period6/12/118/12/11

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