The Block Diagonal Infinite Hidden Markov Model
Abstract
The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states (Beal et al., 2002; Teh et al., 2006). 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.
Cite
Text
Stepleton et al. "The Block Diagonal Infinite Hidden Markov Model." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.Markdown
[Stepleton et al. "The Block Diagonal Infinite Hidden Markov Model." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.](https://mlanthology.org/aistats/2009/stepleton2009aistats-block/)BibTeX
@inproceedings{stepleton2009aistats-block,
title = {{The Block Diagonal Infinite Hidden Markov Model}},
author = {Stepleton, Thomas and Ghahramani, Zoubin and Gordon, Geoffrey and Lee, Tai-Sing},
booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
year = {2009},
pages = {552-559},
volume = {5},
url = {https://mlanthology.org/aistats/2009/stepleton2009aistats-block/}
}