A Robust Minimal Learning Machine based on the M-Estimator

Abstract

In this paper we propose a robust Minimal Learning Machine (R-RLM) for regression problems. The proposed method uses a robust M-estimator to generate a linear mapping between input and output distances matrices of MLM. The R-MLM was tested on one synthetic and three real world datasets that were contaminated with an increasing number of outliers. The method achieved a performance comparable to the robust Extreme Learning Machine (R-RLM) and thus can be seen as a valid alternative for regression tasks on datasets with outliers.