Maximum Correntropy Criterion with Variable Center
This work addresses a specific bottleneck in signal processing and machine learning by enhancing correntropy-based methods, though it appears incremental as it builds directly on existing MCC frameworks.
The authors tackled the limitation of the maximum correntropy criterion (MCC) by extending it to allow variable centers in the kernel function, proposing MCC-VC, and demonstrated its improved performance in regression tasks with linear in parameters models.
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.