On Semi-supervised Learning

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.

Authors and Affiliations

1. Instituto Superior Técnico, Technical University of Lisbon, Portugal

   Mário A. T. Figueiredo

Editors and Affiliations

1. Multimedia Signal Processing and Pattern Recognition Lab., Dept. of Electronics and Communications Eng., Istanbul Technical University, 34469, Istanbul, Turkey

   Bilge Gunsel

2. Department of Computer Science and Engineering, Michigan State University,  

   Anil K. Jain

3. College of Engineering, Koç University, 34450, Sarıyer, İstanbul, Turkey

   A. Murat Tekalp

4. Department of Electrical and Electronics Engineering, Boğaziçi University, Istanbul, Turkey

   Bülent Sankur

Reprints and permissions

© 2006 Springer-Verlag Berlin Heidelberg

Policies and ethics