DocumentCode :
2710239
Title :
Using Correntropy as a cost function in linear adaptive filters
Author :
Singh, Abhishek ; Principe, Jose C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
2950
Lastpage :
2955
Abstract :
Correntropy has been recently defined as a localised similarity measure between two random variables, exploiting higher order moments of the data. This paper presents the use of correntropy as a cost function for minimizing the error between the desired signal and the output of an adaptive filter, in order to train the filter weights.We have shown that this cost function has the computational simplicity of the popular LMS algorithm, along with the robustness that is obtained by using higher order moments for error minimization. We apply this technique for system identification and noise cancellation configurations. The results demonstrate the advantages of the proposed cost function as compared to LMS algorithm, and the recently proposed minimum error entropy (MEE) cost function.
Keywords :
adaptive filters; entropy; identification; least mean squares methods; signal denoising; LMS algorithm; correntropy; cost function; error minimization; higher order moment; least mean square algorithm; linear adaptive filter; minimum error entropy; noise cancellation configuration; signal processing; system identification; Adaptive filters; Adaptive systems; Cost function; Entropy; Least squares approximation; Noise cancellation; Noise robustness; Random variables; Signal processing algorithms; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5178823
Filename :
5178823
Link To Document :
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