SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
This work addresses modeling challenges for sequential data in graph-structured systems, but it appears incremental as it builds on existing HMM and mixture model techniques.
The authors tackled the problem of modeling sequential data from graph-connected entities by proposing a sparse mixture of shared hidden Markov models that leverages graph topology, resulting in demonstrated effectiveness across various domains.
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.