Tech Report A Variational HEM Algorithm for Clustering Hidden Markov Models
Artificial Intelligence
2011-09-07 v1 Machine Learning
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.
Cite
@article{arxiv.1109.1032,
title = {Tech Report A Variational HEM Algorithm for Clustering Hidden Markov Models},
author = {Emanuele Coviello and Antoni B. Chan and Gert R. G. Lanckriet},
journal= {arXiv preprint arXiv:1109.1032},
year = {2011}
}
Comments
13 pages, 1 figure