Minimal Learning Machine: A novel supervised distance-based approach for regression and classification

Abstract

In this work, a novel supervised learning method, the Minimal Learning Machine (MLM), is proposed. Learning in MLM consists in building a linear mapping between input and output distance matrices. In the generalization phase, the learned distance map is used to provide an estimate of the distance from K output reference points to the unknown target output value. Then, the output estimation is formulated as multilateration problem based on the predicted output distance and the locations of the reference points. Given its general formulation, the Minimal Learning Machine is inherently capable of operating on nonlinear regression problems as well as on multidimensional response spaces. In addition, an intuitive extension of the MLM is proposed to deal with classification problems. A comprehensive set of computer experiments illustrates that the proposed method achieves accuracies that are comparable to more traditional machine learning methods for regression and classification thus offering a computationally valid alternative to such approaches.