SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

03/31/2019
by   Diogo Pernes, et al.
0

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 jointly 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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset