A nonlinear principal component analysis of image data

Ryo Saegusa, Hitoshi Sakano, Shuji Hashimoto

    研究成果: Article査読

    7 被引用数 (Scopus)

    抄録

    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 characteristics 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 the principal components. In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.

    本文言語English
    ページ(範囲)2242-2248
    ページ数7
    ジャーナルIEICE Transactions on Information and Systems
    E88-D
    10
    DOI
    出版ステータスPublished - 2005

    ASJC Scopus subject areas

    • Information Systems
    • Computer Graphics and Computer-Aided Design
    • Software

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