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Abstract：
As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel, which is, of course, not always the best choice. In this paper, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel, and present some important properties. We further propose the generalized maximum correntropy criterion (GMCC) and apply it to adaptive filtering. An adaptive algorithm, called the GMCC algorithm, is derived, and the stability problem and steady-state performance are studied. We show that the proposed algorithm is very stable and can achieve zero probability of divergence (POD). Simulation results confirm the theoretical expectations and demonstrate the desirable performance of the new algorithm.
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Source ：
IEEE TRANSACTIONS ON SIGNAL PROCESSING
ISSN： 1053-587X
Year： 2016
Issue： 13
Volume： 64
Page： 3376-3387
4 . 3
JCR@2016
4 . 9 3 1
JCR@2020
ESI Discipline： ENGINEERING;
ESI HC Threshold：128
JCR Journal Grade：1
CAS Journal Grade：2
Cited Count：
WoS CC Cited Count： 375
SCOPUS Cited Count： 510
ESI Highly Cited Papers on the List： 35 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 10
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