Nonlinear principal component analysis to preserve the order of principal components

Ryo Saegusa*, Hitoshi Sakano, Shuji Hashimoto

*この研究の対応する著者

    研究成果: Article査読

    49 被引用数 (Scopus)

    抄録

    Principal component analysis (PCA) is an effective method of linear dimensional reduction. Because of its simplicity in theory and implementation, it is often used for analyses in various disciplines. However, because of its linearity, PCA is not always suitable, and has redundancy in expressing data. To overcome this problem, some nonlinear PCA methods have been proposed. However, most of these methods have drawbacks, such that the number of principal components must be predetermined, and also the order of the generated principal components is not explicitly given. In this paper, we propose a nonlinear PCA algorithm that nonlinearly transforms data into principal components, and at the same time, preserving the order of the principal components, and we also propose a hierarchical neural network model to perform the algorithm. Moreover, our method does not need to know the number of principal components in advance. The effectiveness of the proposed model will be shown through experiments.

    本文言語English
    ページ(範囲)57-70
    ページ数14
    ジャーナルNeurocomputing
    61
    1-4
    DOI
    出版ステータスPublished - 2004 10月

    ASJC Scopus subject areas

    • 人工知能
    • 細胞および分子神経科学

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