780 citations · 1.6k across the 54 of their papers we have counts for
12 papers · 1 filter
Maximum Correntropy Criterion with Variable Center
Badong Chen, Xin Wang, Yingsong Li +1
Correntropy is a local similarity measure defined in kernel space and the maximum correntropy criterion (MCC) has been successfully applied in many areas of signal processing and m…
Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input
Wentao Ma, Dongqiao Zheng, Yuanhao Li +2
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with n…
Quantized Minimum Error Entropy Criterion
Badong Chen, Lei Xing, Nanning Zheng +1
Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing…
Robustness of Maximum Correntropy Estimation Against Large Outliers
Badong Chen, Lei Xing, Haiquan Zhao +2
The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized inste…
Robust Learning with Kernel Mean p-Power Error Loss
Badong Chen, Lei Xing, Xin Wang +2
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecon…
Constrained Maximum Correntropy Adaptive Filtering
Siyuan Peng, Badong Chen, Lei Sun +2
Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have be…
Maximum Correntropy Unscented Filter
Xi Liu, Badong Chen, Bin Xu +2
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximati…
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Badong Chen, Lei Xing, Bin Xu +3
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement ov…
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
Fei Zhu, Abderrahim Halimi, Paul Honeine +2
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixi…
Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
Wentao Ma, Badong Chen, Jiandong Duan +1
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are dev…
Generalized Correntropy for Robust Adaptive Filtering
Badong Chen, Lei Xing, Haiquan Zhao +2
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 max…
Kernel Least Mean Square with Adaptive Kernel Size
Badong Chen, Junli Liang, Nanning Zheng +1
Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually the default kernel in K…