SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
We propose a framework to model the distribution of sequential data coming froma set of entities connected in a graph with a known topology.The method isbased on a mixture of shared hidden Markov models, which are trainedin order to exploit the knowledge of the graph structure and in such a way that theobtained mixtures tend to be sparse.Experiments in different application domainsdemonstrate the effectiveness and versatility of the method.
A method to model the generative distribution of sequences coming from graph connected entities.The authors propose a method to model sequential data from multiple interconnected sources using a mixture of common pool of HMM's.