780 citations · 1.6k across the 54 of their papers we have counts for
6 papers · 1 filter
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…
KLMAT: A Kernel Least Mean Absolute Third Algorithm
Lu Lu, Haiquan Zhao, Badong Chen
In this paper, a kernel least mean absolute third (KLMAT) algorithm is developed for adaptive prediction. Combining the benefits of the kernel method and the least mean absolute th…
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…