• DocumentCode
    3455760
  • Title

    A Unified Framework for Dimensionality Reduction

  • Author

    Ma, Fei ; Chen, Jie

  • Author_Institution
    Sch. of Math. & Comput. Sci., Sun Yat-sen Univ., Guangzhou, China
  • fYear
    2010
  • fDate
    21-23 Oct. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In this paper, we construct a unified framework for dimensionality reduction, for simplicity we call it essential kernel principal component analysis (EKPCA). Some of well-known dimensionality reduction methods, such as kernel principal component analysis, locally linear embedding, Laplacian eigenmaps, Isomaps, diffusion maps are subject to this framework.
  • Keywords
    eigenvalues and eigenfunctions; principal component analysis; Isomaps; Laplacian eigenmaps; diffusion maps; dimensionality reduction; essential kernel principal component analysis; locally linear embedding; Data mining; Eigenvalues and eigenfunctions; Feature extraction; Hilbert space; Kernel; Principal component analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (CCPR), 2010 Chinese Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-7209-3
  • Electronic_ISBN
    978-1-4244-7210-9
  • Type

    conf

  • DOI
    10.1109/CCPR.2010.5659134
  • Filename
    5659134
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3455760