780 citations · 1.8k across the 45 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…