An Effective Support Vector Data Description with Relevant Metric Learning

Support Vector Data Description (SVDD) as a one-class classifier was developed to construct the minimum hypersphere that encloses all the data of the target class in a high dimensional feature space. However, SVDD treats the features of all data equivalently in constructing the minimum hypersphere since it adopts Euclidean distance metric and lacks the incorporation of prior knowledge. In this paper, we propose an improved SVDD through introducing relevant metric learning. The presented method named RSVDD here assigns large weights to the relevant features and tights the similar data. through incorporating the positive equivalence information in a natural way. In practice, we introduce relevant metric learning into the original SVDD model with the covariance matrices of the positive equivalence data.. The experimental results on both synthetic and real data sets show that the proposed method can bring more accurate description for all the tested target cases than the conventional SVDD.