  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 machine learning in recent years. The kernel function
in correntropy is usually restricted to the Gaussian function with center
located at zero. However, zero-mean Gaussian function may not be a good choice
for many practical applications. In this study, we propose an extended version
of correntropy, whose center can locate at any position. Accordingly, we
propose a new optimization criterion called maximum correntropy criterion with
variable center (MCC-VC). We also propose an efficient approach to optimize the
kernel width and center location in MCC-VC. Simulation results of regression
with linear in parameters (LIP) models confirm the desirable performance of the
new method.
