On Semi-supervised Learning

Abstract

In recent years, there has been considerable interest in non-standard learning problems, namely in the so-called semi-supervised learning scenarios. Most formulations of semisupervised learning see the problem from one of two (dual) perspectives: supervised learning (namely, classification) with missing labels; unsupervised learning (namely, clustering) with additional information. In this talk, I will review recent work in these two areas, with special emphasis on our own work. For semi-supervised learning of classifiers, I will describe an approach which is able to incorporate unlabelled data as a regularizer for a (maybe kernel) classifier. Unlike previous approaches, the method is non-transductive, thus computationally inexpensive to use on future data. For semisupervised clustering, I will present a new method, which is able to incorporate pairwise prior information in a computationally efficient way. Finally, I will review recent, as well as potential, applications of semi-supervised learning techniques in multimedia problems.