Date of Original Version



Conference Proceeding

Abstract or Description

The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states . 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.