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

Diogo Pernes, Jaime S. Cardoso

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.

TL;DR: A method to model the generative distribution of sequences coming from graph connected entities.

Keywords: multi-entity sequential data, hidden markov models

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/spamhmm-sparse-mixture-of-hidden-markov/code)