PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The block diagonal infinite hidden markov model
T Stepleton, Zoubin Ghahramani, G Gordon and T. S. Lee
In: AISTATS 2009, 16-18 APR 2009, Florida, USA.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6261
Deposited By:Zoubin Ghahramani
Deposited On:08 March 2010