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

arXiv: https://arxiv.org/abs/1904.00442 · HTML: https://mlsanity.com/paper/1904.00442/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Towards interpretability of Mixtures of Hidden Markov Models](https://mlsanity.com/paper/2103.12576/) (2021)

## Abstract (arXiv)

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.