From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy

Abstract

Support vector machines are among the most popular techniques in machine learning. Given sufficient labeled data, they often yield excellent results. However, for a variety of real-world tasks, the acquisition of sufficient labeled data can be very time-consuming; unlabeled data, on the other hand, can often be obtained easily in huge quantities. Semi-supervised support vector machines try to take advantage of these additional unlabeled patterns and have been successfully applied in this context. However, they induce a hard combinatorial optimization problem. In this work, we present two optimization strategies that address this task and evaluate the potential of the resulting implementations on real-world data sets, including an example from the field of astronomy.