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

    Fingerprint

    Principal component analysis
    Data compression
    Pattern recognition

    Keywords

    • Dimensionality reduction
    • Image
    • Neural network
    • Nonlinear PCA

    ASJC Scopus subject areas

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

    Cite this

    A nonlinear principal component analysis of image data. / Saegusa, Ryo; Sakano, Hitoshi; Hashimoto, Shuji.

    In: IEICE Transactions on Information and Systems, Vol. E88-D, No. 10, 2005, p. 2242-2248.

    Research output: Contribution to journalArticle

    Saegusa, Ryo ; Sakano, Hitoshi ; Hashimoto, Shuji. / A nonlinear principal component analysis of image data. In: IEICE Transactions on Information and Systems. 2005 ; Vol. E88-D, No. 10. pp. 2242-2248.
    @article{2d7939803d434434b4515e1a533922d1,
    title = "A nonlinear principal component analysis of image data",
    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.",
    keywords = "Dimensionality reduction, Image, Neural network, Nonlinear PCA",
    author = "Ryo Saegusa and Hitoshi Sakano and Shuji Hashimoto",
    year = "2005",
    doi = "10.1093/ietisy/e88-d.10.2242",
    language = "English",
    volume = "E88-D",
    pages = "2242--2248",
    journal = "IEICE Transactions on Information and Systems",
    issn = "0916-8532",
    publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
    number = "10",

    }

    TY - JOUR

    T1 - A nonlinear principal component analysis of image data

    AU - Saegusa, Ryo

    AU - Sakano, Hitoshi

    AU - Hashimoto, Shuji

    PY - 2005

    Y1 - 2005

    N2 - 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.

    AB - 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.

    KW - Dimensionality reduction

    KW - Image

    KW - Neural network

    KW - Nonlinear PCA

    UR - http://www.scopus.com/inward/record.url?scp=33645679063&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=33645679063&partnerID=8YFLogxK

    U2 - 10.1093/ietisy/e88-d.10.2242

    DO - 10.1093/ietisy/e88-d.10.2242

    M3 - Article

    AN - SCOPUS:33645679063

    VL - E88-D

    SP - 2242

    EP - 2248

    JO - IEICE Transactions on Information and Systems

    JF - IEICE Transactions on Information and Systems

    SN - 0916-8532

    IS - 10

    ER -