A nonlinear principal component analysis on image data

Ryo Saegusa, Hitoshi Sakano, Shuji Hashimoto

    研究成果: Conference contribution

    2 引用 (Scopus)

    抜粋

    Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristic of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of principal components. In this paper, we reduce the dimensionality of image data with the proposed method, and examine its effectiveness in compression and recognition of the images.

    元の言語English
    ホスト出版物のタイトルMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
    編集者A. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
    ページ589-598
    ページ数10
    出版物ステータスPublished - 2004
    イベントMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis
    継続期間: 2004 9 292004 10 1

    Other

    OtherMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
    Sao Luis
    期間04/9/2904/10/1

    ASJC Scopus subject areas

    • Engineering(all)

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  • これを引用

    Saegusa, R., Sakano, H., & Hashimoto, S. (2004). A nonlinear principal component analysis on image data. : A. Barros, J. Principe, J. Larsen, T. Adali, & S. Douglas (版), Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop (pp. 589-598)