As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and mixture correntropy, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multi-kernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum mixture correntropy criterion (MMCC).

翻译：作为新颖的相似度衡量标准,即两个随机变量之间的内核函数期望值,在强大的机器学习和信号处理中成功应用了碳红树,以对抗大离子。 内核函数在碳红罗皮中通常为零的高斯红内核。 在最近的一项工作中,提出了混合碳红罗皮(MC)的概念,以改善学习性能, 内核函数是混合的Gaussian内核, 即若干宽度不同的零度高森内核的线性组合。 但是, 内核函数的中心始终位于零。 为了进一步提高学习性能, 我们提出了多内核红花(MC)的概念, 混合物的每个组成部分都可以集中在不同的地点, 即若干宽度为零度的高森内核内核内核的线性组合。 正在调查MKC 和混合的内核内核内核, 核心内核内核最大参数 。 实验结果显示在原始标准 MKC 下的自由参数 。