A nonlinear principal component analysis on image data

Ryo Saegusa, Hitoshi Sakano, Shuji Hashimoto

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

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
    EditorsA. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
    Pages589-598
    Number of pages10
    Publication statusPublished - 2004
    EventMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis
    Duration: 2004 Sep 292004 Oct 1

    Other

    OtherMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
    CitySao Luis
    Period04/9/2904/10/1

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    ASJC Scopus subject areas

    • Engineering(all)

    Cite this

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