SOS-HMM: self-organizing structure of hidden Markov model

Authors:
Rakia Jaziri;Mustapha Lebbah;Younès Bennani;Jean-Hugues Chenot
Affiliations:
LIPN, UMR, CNRS, Université Paris 13, Institut National de l'Audiovisuel, Bry-sur-Marne;LIPN, UMR, CNRS, Université Paris 13, Institut National de l'Audiovisuel, Bry-sur-Marne;LIPN, UMR, CNRS, Université Paris 13, Institut National de l'Audiovisuel, Bry-sur-Marne;LIPN, UMR, CNRS, Université Paris 13, Institut National de l'Audiovisuel, Bry-sur-Marne
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

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Abstract

We propose in this paper a novel approach which makes self-organizing maps (SOM) and the Hidden Markov Models (HMMs) cooperate. Our approach (SOS-HMM: Self Organizing Structure of HMM) allows to learn the Hidden Markov Models topology. The main contribution for the proposed approach is to automatically extract the structure of a hidden Markov model without any prior knowledge of the application domain. This model can be represented as a graph of macro-states, where each state represents a micro model. Experimental results illustrate the advantages of the proposed approach compared to a fixed structure approach.