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Open AccessArticle

Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm

by Guobing Qian 1,2,*, Dan Luo 1 and Shiyuan Wang 1,*
1
College of Electronic and Information Engineering, Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China
2
School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
*
Authors to whom correspondence should be addressed.
Entropy 2018, 20(12), 902; https://doi.org/10.3390/e20120902
Received: 4 November 2018 / Revised: 23 November 2018 / Accepted: 23 November 2018 / Published: 25 November 2018
(This article belongs to the Special Issue Information Theoretic Learning and Kernel Methods)
The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stability and obtain the theoretical value of the excess mean square error (EMSE), which are both supported by simulations. Simulation results verify that the proposed RMCKRSL out-performs the MCCC, generalized MCCC (GMCCC), and traditional recursive least squares (RLS). View Full-Text
Keywords: complex; kernel risk-sensitive loss; recursive; stability; EMSE complex; kernel risk-sensitive loss; recursive; stability; EMSE
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Qian, G.; Luo, D.; Wang, S. Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm. Entropy 2018, 20, 902.

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