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 :
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