In keystroke dynamics-based authentication, novelty detection methods have been used since only the valid user’s patterns are available when a classifier is built. After a while, however, impostors’ keystroke patterns become also available from failed login attempts. We propose to retrain the novelty detector with the impostor patterns to enhance the performance. In this paper the support vecto...

We present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better firstand second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable R...

In image retrieval systems, the key point is the description of the set of images. In this paper we show that a representation using a cloud of points offers a flexible description but suffers from class overlap. We propose a novel approach for describing clouds of points based on the support vector data description (SVDD). We show that combining image descriptions using dissimilarities improve...

Support Vector Domain Description (SVDD) is inspired by the Support Vector Classifier. It obtains a sphere shaped decision boundary with minimal volume around a dataset. This data description can be used for novelty or outlier detection. Our approach is always to minimize the volume of the sphere describing the dataset, while at the same time maximize the separability between the spheres. To bu...