• DocumentCode
    1090026
  • Title

    Comments on "Principal component extraction using recursive least squares learning"

  • Author

    Yongfeng Miao

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
  • Volume
    7
  • Issue
    4
  • fYear
    1996
  • fDate
    7/1/1996 12:00:00 AM
  • Firstpage
    1052
  • Abstract
    In the above paper, (Bannour and Azimi-Sanjadi, 1995) we point out and correct flaws in the proofs of the orthonormal property of the optimal weight vectors of a two-layer linear auto-associative network used for sequentially extracting the principal components of a stationary vector stochastic process.
  • Keywords
    covariance matrices; learning (artificial intelligence); multilayer perceptrons; optimal weight vectors; orthonormal property; principal component extraction; recursive least squares learning; stationary vector stochastic process; two-layer linear auto-associative network; Covariance matrix; Eigenvalues and eigenfunctions; Least squares methods; Multilayer perceptrons; Neural networks; Neurons; Resonance light scattering; Stochastic processes; Vectors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.508950
  • Filename
    508950
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1090026