SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
Machine Learning
2019-04-02 v1 Machine Learning
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 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.
Cite
@article{arxiv.1904.00442,
title = {SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities},
author = {Diogo Pernes and Jaime S. Cardoso},
journal= {arXiv preprint arXiv:1904.00442},
year = {2019}
}