SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
Authors: Diogo Pernes, Jaime S. Cardoso
Keywords: multi-entity sequential data, hidden markov models
TL;DR: A method to model the generative distribution of sequences coming from 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.