A consistency-based model selection for one-class classification

Model selection in unsupervised learning is a hard problem. In this paper, a simple selection criterion for hyper-parameters in one-class classifiers (OCCs) is proposed. It makes use of the particular structure of the one-class problem. The mean idea is that the complexity of the classifier is increased until the classifier becomes inconsistent on the target class. This defines the most complex classifier, which can still reliably be trained on the data. Experiments indicated the usefulness of the approach.