A nonlinear subspace method for pattern recognition using a nonlinear PCA

Ryo Saegusa, Shuji Hashimoto

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

    Abstract

    Linear subspace method based on principal component analysis has been applied for pattern recognition of high-dimensional data. However, the linear subspace method can not represent nonlinear characteristics of the data-distribution efficiently. In order to overcome this problem, some nonlinear subspace methods have been proposed. In this paper, we propose a novel nonlinear subspace method for pattern recognition using multi-layered perceptrons which can hierarchically construct a nonlinear subspace from the data-distribution. We introduce the optimization scheme of the subspace taking into account the classification. The proposed method achieves high classi-fication accuracy by the optimization scheme and provides the finite subspace to avoid the crossover of the subspaces. We examine its effectiveness through some experiments.

    Original languageEnglish
    Title of host publicationProceedings of the 8th IASTED International Conference on Signal and Image Processing, SIP 2006
    Pages45-51
    Number of pages7
    Publication statusPublished - 2006
    Event8th IASTED International Conference on Signal and Image Processing, SIP 2006 and the 10th IASTED International Conference on Internet and Multimedia Systems and Applications, IMSA 2006 - Honolulu, HI
    Duration: 2006 Aug 142006 Aug 16

    Other

    Other8th IASTED International Conference on Signal and Image Processing, SIP 2006 and the 10th IASTED International Conference on Internet and Multimedia Systems and Applications, IMSA 2006
    CityHonolulu, HI
    Period06/8/1406/8/16

    Keywords

    • Eigenspace
    • Feature extraction
    • Neural networks
    • Nonlinear PCA
    • Pattern recognition
    • Subspace method

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Signal Processing

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