A Hybrid Self-Organizing Model for Sequence Analysis
Christos Ferles, Andreas Stafylopatis · 2008
The self-organizing hidden Markov model map (SOHMMM) constitutes a cross-section between the theoretic foundations and algorithmic realizations of the self-organizing map (SOM) and the hidden Markov model (HMM). The intimate fusion and synergy of the SOM unsupervised training and HMM dynamic programming algorithms brings forth a novel on-line gradient descent learning algorithm, which is fully integrated into the SOHMMM. The model is presented from both a theoretical and algorithmic perspective. The SOHMMM can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification.