Equivalence of non-iterative algorithms for simultaneous low rank approximations of matrices

Kohei Inoue, Kiichi Urahama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    22 Citations (Scopus)

    Abstract

    Recently four non-iterative algorithms for simultaneous low rank approximations of matrices (SLRAM) have been presented by several researchers. In this paper, we show that those algorithms are equivalent to each other because they are reduced to the eigenvalue problems of row-row and column-column covariance matrices of given matrices. Also, we show a relationship between the non-iterative algorithms and another algorithm which is claimed to be an analytical algorithm for the SLRAM, Experimental results show that the analytical algorithm does not necessarily give the optimal solution of the SLRAM.

    Original languageEnglish
    Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
    Pages154-159
    Number of pages6
    DOIs
    Publication statusPublished - 2006
    Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
    Duration: Jun 17 2006Jun 22 2006

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume1
    ISSN (Print)1063-6919

    Other

    Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
    Country/TerritoryUnited States
    CityNew York, NY
    Period6/17/066/22/06

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition

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