Home Page

Papers

Submissions

News

Scope

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

The Block Diagonal Infinite Hidden Markov Model

Thomas Stepleton, Zoubin Ghahramani, Geoffrey Gordon, Tai-Sing Lee; JMLR W&CP 5:552-559, 2009.

Abstract

The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states \cite{ihmm,hdp}. We present a generalization of this framework that introduces block-diagonal structure in the transitions between the hidden states. These blocks correspond to "sub-behaviors" exhibited by data sequences. In identifying such structure, the model classifies, or partitions, sequence data according to these sub-behaviors in an unsupervised way. We present an application of this model to artificial data, a video gesture classification task, and a musical theme labeling task, and show that components of the model can also be applied to graph segmentation.



Home Page

Papers

Submissions

News

Scope

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Page last modified on Fri Apr 3 20:30:46 BST 2009.

Copyright @ JMLR 2000. All rights reserved.