• DocumentCode
    3494355
  • Title

    Nonlinear dimensionality reduction with input distances preservation

  • Author

    Garrido, Lluís ; Gomez, Sergio ; Roca, Jaume

  • Author_Institution
    Dept. d´´Estructura i Constituents de la Materia, Barcelona Univ., Spain
  • Volume
    2
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    922
  • Abstract
    A new error term for dimensionality reduction, which clearly improves the quality of nonlinear principal component analysis neural networks, is introduced, and some illustrative examples are given. The method maintains the original data structure by preserving the distances between data points
  • Keywords
    neural nets; data structure; input distances preservation; multidimensional data analysis; neural networks; nonlinear dimensionality reduction; principal component analysis;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)
  • Conference_Location
    Edinburgh
  • ISSN
    0537-9989
  • Print_ISBN
    0-85296-721-7
  • Type

    conf

  • DOI
    10.1049/cp:19991230
  • Filename
    818055
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3494355