Sparseness achievement in Hidden Markov Models

In this paper, a novel learning algorithm for Hidden
Markov Models (HMMs) has been devised. The key issue
is the achievement of a sparse model, i.e., a model in which
all irrelevant parameters are set exactly to zero. Alternatively
to standard Maximum Likelihood Estimation (Baum
Welch training), in the proposed approach the parameters
estimation problem is cast into a Bayesian framework, with
the introduction of a negative Dirichlet prior, which strongly
encourages sparseness of the model. A modified Expectation
Maximization algorithm has been devised, able to
determine a MAP (Maximum A Posteriori probability) estimate
of HMM parameters in this Bayesian formulation.
Theoretical considerations and experimental comparative
evaluations on a 2D shape classification task contribute to
validate the proposed technique.