• DocumentCode
    51354
  • Title

    Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion

  • Author

    Badong Chen ; Jianji Wang ; Haiquan Zhao ; Nanning Zheng ; Principe, Jose C.

  • Author_Institution
    Inst. of Artificial Intell. & Robot., Xi´an Jiaotong Univ., Xi´an, China
  • Volume
    22
  • Issue
    10
  • fYear
    2015
  • fDate
    Oct. 2015
  • Firstpage
    1723
  • Lastpage
    1727
  • Abstract
    The maximum correntropy criterion (MCC) has received increasing attention in signal processing and machine learning due to its robustness against outliers (or impulsive noises). Some gradient based adaptive filtering algorithms under MCC have been developed and available for practical use. The fixed-point algorithms under MCC are, however, seldom studied. In particular, too little attention has been paid to the convergence issue of the fixed-point MCC algorithms. In this letter, we will study this problem and give a sufficient condition to guarantee the convergence of a fixed-point MCC algorithm.
  • Keywords
    adaptive filters; entropy; fixed point arithmetic; gradient methods; fixed-point MCC algorithms; fixed-point algorithm; gradient based adaptive filtering algorithms; machine learning; maximum correntropy criterion; signal processing; sufficient condition; Adaptive filters; Algorithm design and analysis; Convergence; Machine learning algorithms; Robustness; Signal processing algorithms; Sufficient conditions; Fixed-point algorithm; maximum correntropy criterion; robust estimation;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2015.2428713
  • Filename
    7100862