Robust multilinear principal component analysis

Kohei Inoue, Kenji Hara, Kiichi Urahama

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

    15 Citations (Scopus)


    We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.

    Original languageEnglish
    Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
    Number of pages7
    Publication statusPublished - Dec 1 2009
    Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
    Duration: Sept 29 2009Oct 2 2009

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision


    Other12th International Conference on Computer Vision, ICCV 2009

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition


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