A Novel T2-SVM for Partially Supervised Classification

This paper addresses partially supervised classification problems, i.e. problems in which different data sets referring to the same scenario (phenomenon) should be classified but a training information is available only for some of them. In particular, we propose a novel approach to the partially supervised classification which is based on a Bi-transductive Support Vector Machines (T2-SVM). Inspired by recently proposed Transductive SVM (TSVM) and Progressive Transductive SVM (PTSVM) algorithms, the T2-SVM algorithm extracts information from unlabeled samples exploiting the transductive inference, thus obtaining high classification accuracies. After defining the formulation of the proposed T2-SVM technique, we also present a novel accuracy assessment strategy for the validation of the classification performances. The experimental results carried out on a real remote sensing partially supervised problem confirmed the reliability and the effectiveness of both the T2-SVM and the corresponding validation procedure.