A flexible approach to high-dimensional clustering and classification using mixtures of finite mutually repulsive mixtures

We propose a novel approach for clustering that is later extended to classification. Our methods are based on an underlying determinantal point process (DPP) that generates sparsity and separation of location parameters in a mixture of finite mixture models specification. A key aspect is the assumpt