• DocumentCode
    3402907
  • Title

    Application of Kernel Principal Components Analysis to pattern recognitions

  • Author

    Sohara, Kosuke ; Kotani, Manabu

  • Author_Institution
    Fac. of Eng., Kobe Univ., Japan
  • Volume
    2
  • fYear
    2002
  • fDate
    5-7 Aug. 2002
  • Firstpage
    750
  • Abstract
    Kernel Principal Component Analysis (Kernel PCA) is one of the methods to perform PCA in high dimensional space. The purpose of this paper is to examine what components are obtained by Kernel PCA and evaluate effectiveness of the components as feature. Simulation´s results show that Kernel PCA can get superior performance to PCA.
  • Keywords
    eigenvalues and eigenfunctions; learning automata; pattern recognition; principal component analysis; Kernel PCA; Kernel Principal Component Analysis; Principal Component Analysis; pattern recognitions; support vector machine; Data mining; Eigenvalues and eigenfunctions; Kernel; Pattern analysis; Pattern recognition; Performance analysis; Polynomials; Principal component analysis; Spatial databases; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE 2002. Proceedings of the 41st SICE Annual Conference
  • Print_ISBN
    0-7803-7631-5
  • Type

    conf

  • DOI
    10.1109/SICE.2002.1195250
  • Filename
    1195250
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3402907