Discriminant Support Vector Data Description

Abstract

Support Vector Data Description (SVDD) was designed to construct a minimum hypersphere so as to enclose all the data of the target class in the one-class classification case. In this paper, we propose a novel Discriminant Support Vector Data Description (DSVDD). The proposed DSVDD adopts the relevant metric learning instead of the original Euclidean distance metric learning in SVDD, where the relevant metric learning can consider the relationship between data. Here through incorporating both the positive and negative equivalence information, the presented DSVDD assigns large weights to the relevant features and tightens the similar data. More importantly, we introduce the discriminant knowledge prior into the proposed algorithm due to considering the negative equivalence information. The experiments show that the proposed DSVDD can bring more accurate classification performance than the conventional SVDD for all the tested data.