Robust Learning With Kernel Mean <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Power Error Loss
提出一种核空间中的非二阶统计量——核均值p次幂误差，并应用于极限学习机和主成分分析，开发出两种鲁棒学习算法，实验表明其性能优于现有方法。
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean- power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve better performance when compared with some existing methods.