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http://dx.doi.org/10.5762/KAIS.2021.22.2.714

A Study on Kernel Size Adaptation for Correntropy-based Learning Algorithms  

Kim, Namyong (School of Electronic, Information & Communications Eng, Kangwon Univ.)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.2, 2021 , pp. 714-720 More about this Journal
Abstract
The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.
Keywords
Correntropy; MCC; Kernel size; Impulsive noise; Constant;
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