# Maximum Correntropy Criterion with Variable Center

## TL;DR

This paper introduces a novel extension of the maximum correntropy criterion that allows the kernel center to vary, improving flexibility and performance in signal processing tasks.

## Contribution

The paper proposes MCC-VC, an extended correntropy measure with a variable kernel center, along with an optimization approach for kernel parameters.

## Key findings

- Enhanced regression performance in simulations
- Flexible kernel positioning improves robustness
- Efficient optimization of kernel parameters

## Abstract

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.