• DocumentCode
    1906727
  • Title

    Fast nonlinear dimension reduction

  • Author

    Kambhatla, Nandakishore ; Leen, Todd K.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Oregon Graduate Inst. of Sci. & Technol., Beaverton, OR, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    1213
  • Abstract
    A new algorithm for nonlinear dimension reduction is presented. The algorithm builds a piecewise linear model of the data. It provides compression that is superior to the globally linear model produced by principal component analysis. On several examples the piecewise linear model also provides compression that is superior to the global nonlinear model constructed by a five-layer, autoassociative neural network. The new algorithm trains significantly faster than the autoassociative network
  • Keywords
    data compression; neural nets; piecewise-linear techniques; compression; neural network; nonlinear dimension reduction; piecewise linear model; Computer science; Data analysis; Drives; Ear; Neural networks; Pattern recognition; Piecewise linear techniques; Principal component analysis; Redundancy; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298730
  • Filename
    298730
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1906727