Semi-supervised subclass support vector data description for image and video classification

Abstract

In this paper, the Semi-Supervised Subclass Support Vector Data Description is presented, a method that operates in both the supervised and the semi-supervised One-class classification case. The proposed method consists a novel extension of the standard SVDD method, by introducing two additional terms its optimization problem. These two terms correspond to expressing global and local geometric data information respectively, during the classifier optimization process. Global geometric data information is employed by minimizing the global target class variance, assuming that subclasses may have been formed within as well. In addition, by exploiting the semi-supervised learning smoothness assumption, local neighborhood information between all available (labeled and unlabeled) data is preserved, even in the supervised learning case. We show that the adoption of both terms results in a regularized feature space, where low variance directions have been emphasized, while local geometric data information have been preserved. The proposed method has been evaluated in classification problems related to face recognition, human action recognition and generic One-class classification problems, comparing favorably against related One-class classification methods in both the semi-supervised and the supervised learning cases.