Prototype-based One-Class-Classification Learning Using Local Representations

One-class-classification remains an important problem in machine learning, which is related to data representation and outlier detection, but different from them in several aspects. In the present contribution we propose an one-class-classifier based on a prototype vector quantization model. We modeled a corresponding cost function to account for aspects of representation learning and to appropriately evaluate the one-class classifier. The prototype-based model ensures a local representation of the target class. After this introduction, we obtain an interpretable one-class classifier model. We demonstrate the capabilities of the approach by applying the classifier to illustrative toy data examples as well as on real data in a medical context.