LAUSR

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 the center located at zero. However, the zero-mean Gaussian function may not be a good choice for many practical applications. In this letter, we propose an extended version of correntropy, whose center can be located 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 the MCC-VC. Simulation results of regression with linear-in-parameter (LIP) models confirm the desirable performance of the new method.