Novelty Discovery with Kernel Minimum Enclosing Balls

We introduce the idea of utilizing ensembles of Kernel Minimum Enclosing Balls to detect novel datapoints. To this end, we propose a novelty scoring methodology that is based on combining outcomes of the corresponding characteristic functions of a set of fitted balls. We empirically evaluate our model by presenting experiments on synthetic as well as real world datasets.