Fast State Discovery for HMM Model Selection and Learning (2007)

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.

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Approximate BibTeX Entry

@proceedings{siddiqi_aistats07,
    Year = {2007},
    Booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AI-STATS)},
    Author = { Sajid Siddiqi Geoff Gordon Andrew Moore },
    Title = {Fast State Discovery for HMM Model Selection and Learning}
}