A minimal learning machine for datasets with missing values

Abstract

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with simple implementation and few hyper parameters. Learning MLM model consists on building a linear mapping between input and output distance matrices. In this work, the standard MLM is modified to deal with missing data. For that, the expected squared distance approach is used to compute the input space distance matrix. The proposed approach showed promising results when compared to standard strategies that deal with missing data.