Publication Date:
2007
Short description:
Sparseness achievement in Hidden Markov Models / Bicego, Manuele; Cristani, Marco; Murino, Vittorio. - (2007), pp. 67-72. ( Proceedings 14th International Conference on Image Analysis and Processing) [10.1109/ICIAP.2007.4362759].
abstract:
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.
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.
Iris type:
4.1 Contributo in Atti di convegno
Keywords:
Hidden Markov model; classification
List of contributors: