Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering

Abstract In the problem of one-class classification, one-class classifier (OCC) tries to identify objects of a specific class, called the target class, among all objects, by learning from a training set containing only the objects of target class. However, some traditional OCCs lose their effectiveness when the distribution of target class is a multimodal distribution, and the performance is sensitive to the model parameters. To solve this problem and enhance classification performance, a novel OCC infinite Bayesian OCSVM (IB-OCSVM) is proposed in this study. In IB-OCSVM, we partition the input data into several clusters with the Dirichlet process mixture (DPM), and learn a modified OCSVM in each cluster. Specifically, the clustering procedure and the modified OCSVM are jointly learned in a unified Bayesian frame to guarantee the consistency of clustering and linear separability in each cluster. The parameters can be inferred simply and effectively via Gibbs sampling technique. Meanwhile, we modify the traditional OCSVM by replacing the kernel transformation with a new feature transformation based on the result of clustering via DPM. In the feature space of the modified OCSVM, all transformed samples preserve the same clustering characteristic as the original samples, thus guaranteeing the combination of clustering and classification in our method. Experimental results based on benchmark datasets and synthetic aperture radar real data demonstrate that the proposed method has enhanced classification performance and is more robust to the model parameters than some conventional methods.