A nonlinear principal component analysis of image data

Ryo Saegusa, Hitoshi Sakano, Shuji Hashimoto

    Research output: Contribution to journalArticle

    6 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 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.

    Original languageEnglish
    Pages (from-to)2242-2248
    Number of pages7
    JournalIEICE Transactions on Information and Systems
    VolumeE88-D
    Issue number10
    DOIs
    Publication statusPublished - 2005

    Keywords

    • Dimensionality reduction
    • Image
    • Neural network
    • Nonlinear PCA

    ASJC Scopus subject areas

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

    Fingerprint Dive into the research topics of 'A nonlinear principal component analysis of image data'. Together they form a unique fingerprint.

  • Cite this