Kernel Risk-Sensitive Mean p-Power Error Algorithms for Robust Learning

As a nonlinear similarity measure defined in the reproducing kernel Hilbert space (RKHS), the correntropic loss (C-Loss) has been widely applied in robust learning and signal processing. However, the highly non-convex nature of C-Loss results in performance degradation. To address this issue, a conv...

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Bibliographic Details
Published in:	Entropy (Basel, Switzerland) Vol. 21; no. 6; p. 588
Main Authors:	Zhang, Tao, Wang, Shiyuan, Zhang, Haonan, Xiong, Kui, Wang, Lin
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 13.06.2019 MDPI
Subjects:	Accuracy Adaptive filters Algorithms Approximation correntropic Exponential functions Hilbert space kernel adaptive filters kernel risk-sensitive mean p-power error Kernels Machine learning Performance degradation quantized Random variables recursive Risk Robustness Signal processing Similarity Similarity measures
ISSN:	1099-4300, 1099-4300
Online Access:	Get full text
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