Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences

Brice Olivier 1 Anne Guérin-Dugué 2 Jean-Baptiste Durand 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 CLIPS - IMAG - Communication Langagière et Interaction Personne-Système
Abstract : In this study, we propose a method called sequence breaking framework to search high local maximum of the likelihood by providing starting values based on the observations for the Expectation-Maximization algorithm, for Hidden semi-Markov model parameter estimation. The method is shown to be efficient on several datasets with multiple categorical sequences.
Keywords : multiple categorical sequences Hidden semi-Markov models choosing starting values for the EM algorithm
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Conference papers
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Brice Olivier, Anne Guérin-Dugué, Jean-Baptiste Durand. Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences. JdS 2019 - 51èmes Journées de Statistique, Jun 2019, Vandœuvre-lès-Nancy, France. pp.1-7. ⟨hal-02129122⟩

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