Clustering Hidden Markov Models with Variational HEM

Provided by: Journal of Machine Learning Research (JMLR)
Topic: Big Data
Format: PDF
The Hidden Markov Model (HMM) is a widely-used generative model that copes with sequential data, assuming that each observation is conditioned on the state of a hidden Markov chain. In this paper, the authors derive a novel algorithm to cluster HMMs based on the Hierarchical EM (HEM) algorithm. The proposed algorithm clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent and characterizes each group by a \"Cluster center\", that is, a novel HMM that is representative for the group, in a manner that is consistent with the underlying generative model of the HMM.

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