Support vector data description using privileged information

Support vector data description (SVDD) is a data description method which gives the target data set a hypersphere-shaped description and can be used for one-class classification or outlier detection. To further improve its performance, a novel SVDD called SVDD+ which introduces the privileged information to the traditional SVDD is proposed. This privileged information, which is ignored by the classical SVDD but often exists in human learning, will optimise the training phase by constructing a set of correcting functions. The performance of SVDD+ on data sets from the UCI machine learning repository and radar emitter recognition is demonstrated. The experimental results indicate the validity and advantage of this method.