• DocumentCode
    1124838
  • Title

    Gradient-Type Algorithms for Partial Singular Value Decomposition

  • Author

    Haimi-Cohen, Raziel ; Cohen, Arnon

  • Author_Institution
    Department of Electrical and Computer Engineering, Ben-Gurion University, Beer-Sheva, Israel; Tadiran, Inc., Telecommunication Divison, P. O. B. 500, Petah Tikva 49104, Israel.
  • Issue
    1
  • fYear
    1987
  • Firstpage
    137
  • Lastpage
    142
  • Abstract
    It is often desirable to calculate only a few terms of the SVD expansion of a matrix, corresponding to the largest or smallest singular values. Two algorithms, based on gradient and conjugate gradient search, are proposed for this purpose. SVD is computed term by term in a decreasing or increasing order of singular values. The algorithms are simple to implement and are especially advantageous with large matrices.
  • Keywords
    Biomedical signal processing; Councils; Covariance matrix; Eigenvalues and eigenfunctions; Matrix decomposition; Pattern analysis; Psychology; Signal processing algorithms; Singular value decomposition; Symmetric matrices; Conjugate gradient; Rayleigh quotient; gradient search; partial singular value decomposition;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1987.4767879
  • Filename
    4767879
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1124838