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

Diogo Pernes; Jaime S. Cardoso

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arXiv · 1904.00442

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

Diogo Pernes·Jaime S. Cardoso

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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.

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Diogo Pernes, Jaime S. Cardoso. 2019-03-31. SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities. https://arxiv.org/abs/1904.00442

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