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