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
Link To Document