Semi-Supervised Multiclass Kernel Machines with Probabilistic Constraints

Abstract

The extension of kernel-based binary classifiers to multiclass problems has been approached with different strategies in the last decades. Nevertheless, the most frequently used schemes simply rely on different criteria to combine the decisions of a set of independently trained binary classifiers. In this paper we propose an approach that aims at establishing a connection in the training stage of the classifiers using an innovative criterion. Motivated by the increasing interest in the semi-supervised learning framework, we describe a soft-constraining scheme that allows us to include probabilistic constraints on the outputs of the classifiers, using the unlabeled training data. Embedding this knowledge in the learning process can improve the generalization capabilities of the multiclass classifier, and it leads to a more accurate approximation of a probabilistic output without an explicit post-processing. We investigate our intuition on a face identification problem with 295 classes.