Fast state discovery for HMM model selection and learning (2007)
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| Venue: | In Proc. Int’l Conference on Artificial Intelligence and Statistics |
| Citations: | 7 - 2 self |
BibTeX
@INPROCEEDINGS{Siddiqi07faststate,
author = {Sajid M. Siddiqi and Geoffrey J. Gordon and Andrew W. Moore},
title = {Fast state discovery for HMM model selection and learning},
booktitle = {In Proc. Int’l Conference on Artificial Intelligence and Statistics},
year = {2007}
}
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Abstract
Choosing the number of hidden states and their topology (model selection) and estimating model parameters (learning) are important problems for Hidden Markov Models. This paper presents a new state-splitting algorithm that addresses both these problems. The algorithm models more information about the dynamic context of a state during a split, enabling it to discover underlying states more effectively. Compared to previous top-down methods, the algorithm also touches a smaller fraction of the data per split, leading to faster model search and selection. Because of its efficiency and ability to avoid local minima, the state-splitting approach is a good way to learn HMMs even if the desired number of states is known beforehand. We compare our approach to previous work on synthetic data as well as several real-world data sets from the literature, revealing significant improvements in efficiency and test-set likelihoods. We also compare to previous algorithms on a sign-language recognition task, with positive results. 1
