A Variational HEM Algorithm for Clustering Hidden Markov Models by: Emanuele Coviello, Antoni B. Chan, and Gert R.G. Lanckriet.
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.
Warning: Heavy sledding but the examples of improved hierarchical motion clustering, music tagging, and online hand writing recognition are quite compelling.