• DocumentCode
    2215801
  • Title

    A class of PCA learning algorithms and their convergence

  • Author

    Zhang, Yu

  • Author_Institution
    Dept. of Comput. Eng., Chengdu Aeronaut. Vocational & Tech. Coll., Chengdu, China
  • Volume
    1
  • fYear
    2010
  • fDate
    20-22 Aug. 2010
  • Abstract
    This paper proposes a class of principal component analysis (PCA) learning algorithms with constant learning rates. It will prove via deterministic discrete time (DDT) method that these PCA learning algorithms are globally convergent.
  • Keywords
    convergence; learning (artificial intelligence); neural nets; principal component analysis; PCA learning algorithms; constant learning rates; convergence; deterministic discrete time method; principal component analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
  • Conference_Location
    Chengdu
  • ISSN
    2154-7491
  • Print_ISBN
    978-1-4244-6539-2
  • Type

    conf

  • DOI
    10.1109/ICACTE.2010.5579030
  • Filename
    5579030
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2215801