Mean-Variance Minimization Regularized Extreme Learning Machine With Maximum Correntropy Criterion

Abstract Extreme learning machine (ELM) has gained significant attention for its remarkable generalization performance and fast processing speeds in recent years. However, its sensitivity to non-Gaussian noise and outliers has been a notable limitation. In this article, we incorporate correntropy loss and modeling error distributions in ELM training and propose a robust ELM that minimizes the mean and variance of modeling errors with maximum correntropy criterion, aimed at improving modeling performance in a noisy environment. Correntropy, as a robust generalized nonlinear similarity measure, is developed to capture the variance of errors in the modeling process, while the integration of the half-quadratic optimization technique ensures efficiency of the proposed ELM training. The robust and parameter sensitivity analysis of the proposed framework further reinforces its reliability, while experimental results on benchmark datasets confirm its superior generalization performance, even in the presence of varying ratios of outliers.