Equilibrium-Based Support Vector Machine for Semisupervised Classification

Abstract

A novel learning algorithm for semisupervised classification is proposed. The proposed method first constructs a support function that estimates a support of a data distribution using both labeled and unlabeled data. Then, it partitions a whole data space into a small number of disjoint regions with the aid of a dynamical system. Finally, it labels the decomposed regions utilizing the labeled data and the cluster structure described by the constructed support function. Simulation results show the effectiveness of the proposed method to label out-of-sample unlabeled test data as well as in-sample unlabeled data.