A nonlinear subspace method for pattern recognition using a nonlinear PCA

Ryo Saegusa, Shuji Hashimoto

    研究成果: Conference contribution

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルProceedings of the 8th IASTED International Conference on Signal and Image Processing, SIP 2006
    ページ45-51
    ページ数7
    出版ステータスPublished - 2006
    イベント8th 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
    継続期間: 2006 8 142006 8 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

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Signal Processing

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