  As a novel similarity measure that is defined as the expectation of a kernel
function between two random variables, correntropy has been successfully
applied in robust machine learning and signal processing to combat large
outliers. The kernel function in correntropy is usually a zero-mean Gaussian
kernel. In a recent work, the concept of mixture correntropy (MC) was proposed
to improve the learning performance, where the kernel function is a mixture
Gaussian kernel, namely a linear combination of several zero-mean Gaussian
kernels with different widths. In both correntropy and mixture correntropy, the
center of the kernel function is, however, always located at zero. In the
present work, to further improve the learning performance, we propose the
concept of multi-kernel correntropy (MKC), in which each component of the
mixture Gaussian kernel can be centered at a different location. The properties
of the MKC are investigated and an efficient approach is proposed to determine
the free parameters in MKC. Experimental results show that the learning
algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can
outperform those under the original maximum correntropy criterion (MCC) and the
maximum mixture correntropy criterion (MMCC).
