On the evaluation of a nonlinear Principal Component Analysis

Ryo Saegusa, Shuji Hashimoto

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    Principal Component Analysis (PCA) is a useful method in multivariate analysis to reduce the dimensionality of data. We have already proposed a non-linearly extended model of PCA by employing neural networks and have shown its effectiveness with some artificial data. In this paper, we report results of a nonlinear principal component analysis on real-world data utilizing the proposed method. Moreover, we compare the distribution of reconstructed data with the distribution of the original data to discuss the advantage of nonlinear PCA.

    本文言語English
    ホスト出版物のタイトルProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
    編集者M.H. Hamza
    ページ66-72
    ページ数7
    出版ステータスPublished - 2004
    イベントProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald
    継続期間: 2004 2 232004 2 25

    Other

    OtherProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
    CityGrindelwald
    Period04/2/2304/2/25

    ASJC Scopus subject areas

    • 工学(全般)

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