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

• Type: Conference
• Year: 2007
• Where: ICIAP
• Authors: Manuele Bicego, Marco Cristani, Vittorio Murino
• Keywords: Baum Welch Training, ICIAP 2007, Image Processing, Maximum Likelihood Estimation, Parameters Estimation Problem