• DocumentCode
    3515404
  • Title

    Least-Square Regularized Regression in Compressed Domain

  • Author

    Lu, Weijun ; Tang, Yi ; Chen, Hong

  • Author_Institution
    Sch. of Comput. Sci., Wuhan Univ. of Sci. & Eng., Wuhan, China
  • fYear
    2010
  • fDate
    28-29 Oct. 2010
  • Firstpage
    93
  • Lastpage
    96
  • Abstract
    This paper considers the regularized learning algorithm associated with the least-square loss and compressed domain. The target is the error analysis for the regression problem learned in compressed domain. We show that the least-square regularized algorithm is beneficial from the compressed sensing.
  • Keywords
    data compression; error analysis; learning (artificial intelligence); regression analysis; compressed domain; error analysis; least-square regression method; loss domain; regularized learning algorithm; Algorithm design and analysis; Complexity theory; Compressed sensing; Distortion measurement; Machine learning; Presses; Training; compressed learning; least square regression; sparsity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on
  • Conference_Location
    Huanggang
  • Print_ISBN
    978-1-4244-8148-4
  • Electronic_ISBN
    978-0-7695-4196-9
  • Type

    conf

  • DOI
    10.1109/IPTC.2010.27
  • Filename
    5663186
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3515404