Title: SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities. Abstract: We propose a framework to model the distribution of sequential data coming from
a set of entities connected in a graph with a known topology. The method is
based on a mixture of shared hidden Markov models (HMMs), which are trained
in order to exploit the knowledge of the graph structure and in such a way that the
obtained mixtures tend to be sparse. Experiments in different application domains
demonstrate the effectiveness and versatility of the method.