# Maximum Correntropy Criterion with Variable Center

arXiv: https://arxiv.org/abs/1904.06501 · HTML: https://mlsanity.com/paper/1904.06501/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Error Loss Networks](https://mlsanity.com/paper/2106.03722/) (2021)
- [Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center](https://mlsanity.com/paper/2210.12915/) (2022)
- [Structured Adaptive Tensor Prediction for Streaming Data](https://mlsanity.com/paper/2606.10085/) (2026)

## Abstract (arXiv)

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.