Tech Report A Variational HEM Algorithm for Clustering Hidden Markov Models

TL;DR

This paper introduces a hierarchical EM algorithm for clustering multiple HMMs based on their probability distributions, providing a way to group similar models and identify representative cluster centers.

Contribution

A novel hierarchical EM algorithm for clustering HMMs and defining representative cluster centers based on their distributions.

Findings

Effective clustering of HMMs demonstrated in empirical studies

Cluster centers accurately represent groups of similar HMMs

Algorithm improves model organization and interpretability

Abstract

The hidden Markov model (HMM) is a generative model that treats sequential data under the assumption that each observation is conditioned on the state of a discrete hidden variable that evolves in time as a Markov chain. In this paper, we derive a novel algorithm to cluster HMMs through their probability distributions. We propose a hierarchical EM algorithm that i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a "cluster center", i.e., a novel HMM that is representative for the group. We present several empirical studies that illustrate the benefits of the proposed algorithm.