Generalized support vector data description for anomaly detection

Highlights：

• In this paper, a generalized SVDD procedure called GSVDD for multi-class data is introduced. The proposed procedure finds n hyperspheres. GSVDD uses each class information. Thus, each hypersphere keeps as many as corresponding observations as possible inside the boundary and attempts to keep other classes’ observations outside the hypersphere.

• In addition, Bayesian generalized SVDD procedure is proposed by considering a probabilistic behavior of the parameters by taking ‘prior knowledge’ into account. This procedure enables each observation to have a probabilistic information. Thus, these probabilities are used for classification.

• Although there are some existing multi-class SVDD procedures, they do not consider the information from each class. Moreover, none of these procedures do not provide probabilistic interpretation.