An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models
This work addresses model selection challenges in semi-supervised learning for researchers, but it is incremental as it builds on existing EM and simulated annealing techniques.
The authors tackled the combinatorial optimization problem in model selection for non-homogeneous hidden Markov models, which involves 4^p potential configurations, by proposing an adaptive simulated annealing EM algorithm for joint optimization, resulting in an efficient method for inference.
Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent states and mean parameter of the probability distribution of the responses (for a given state) depend on the set of $p$ covariates. A priori we do not know which (and how) covariates influence the transition probabilities and the mean parameters. This induces a complex combinatorial optimization problem for model selection with $4^p$ potential configurations. To address the problem, in this article we propose an adaptive (A) simulated annealing (SA) expectation maximization (EM) algorithm (ASA-EM) for joint optimization of models and their parameters with respect to a criterion of interest.