标题： 最大互信息准则与可变中心 显示英文标题

标题： Maximum Correntropy Criterion with Variable Center

摘要： 相关熵是在核空间中定义的一种局部相似性度量，最大相关熵准则（MCC）近年来已被成功应用于信号处理和机器学习的许多领域。相关熵中的核函数通常被限制为以零为中心的高斯函数。然而，零均值高斯函数可能不是许多实际应用的良好选择。在本研究中，我们提出了一种扩展的相关熵版本，其中心可以位于任何位置。相应地，我们提出了一种新的优化准则，称为具有可变中心的最大相关熵准则（MCC-VC）。我们还提出了一种有效的方法来优化MCC-VC中的核宽度和中心位置。线性参数（LIP）模型的回归仿真结果证实了新方法的优良性能。 显示英文摘要

摘要： 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.