Generalized Correntropy for Robust Adaptive Filtering

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...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 64; no. 13; pp. 3376 - 3387
Main Authors: Badong Chen, Lei Xing, Haiquan Zhao, Nanning Zheng, Principe, Jose C.
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
adaptive filtering
Adaptive filters
Algorithms
Correntropy
Cost function
Criteria
Filtering
Gaussian
generalized correntropy
GMCC algorithm
Kernel
Kernels
Robustness
Signal processing
Signal processing algorithms
Similarity
Stability criteria
Steady-state
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary: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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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2016.2539127