Sparse minimal learning machine using a diversity measure minimization

Abstract

The minimal learning machine (MLM) training procedure consists in solving a linear system with multiple measurement vectors (MMV) created between the geometric congurations of points in the input and output spaces. Such geometric congurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression settings. Experiments were carried out using UCI datasets, where the proposal was able to produce sparser models and achieve competitive performance when compared to the regular strategy of selecting MLM input RPs.