Hidden Markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs

Yasuo Matsuyama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    9 Citations (Scopus)

    Abstract

    Fast estimation algorithms for Hidden Markov models (HMMs) for given data are presented. These algorithms start from the alpha-EM algorithm which includes the traditional log-EM as its proper subset. Since existing or traditional HMMs are the outcome of the log-EM, it had been expected that the alpha-HMM would exist. In this paper, it is shown that this foresight is true by using methods of the iteration index shift and likelihood ratio expansion. In each iteration, new update equations utilize one-step past terms which are computed and stored during the previous maximization step. Therefore, iteration speedup directly appears as that of CPU time. Since the new method is theoretically based on the alpha-EM, all of its properties are inherited. There are eight types of alpha-HMMs derived. They are discrete, continuous, semi-continuous and discrete-continuous alpha-HMMs, and both for single and multiple sequences. Using the properties of the alpha-EM algorithm, the speedup property is theoretically analyzed. Experimental results including real world data are given.

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Pages808-816
    Number of pages9
    DOIs
    Publication statusPublished - 2011
    Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA
    Duration: 2011 Jul 312011 Aug 5

    Other

    Other2011 International Joint Conference on Neural Network, IJCNN 2011
    CitySan Jose, CA
    Period11/7/3111/8/5

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    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

    Cite this

    Matsuyama, Y. (2011). Hidden Markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs. In Proceedings of the International Joint Conference on Neural Networks (pp. 808-816). [6033304] https://doi.org/10.1109/IJCNN.2011.6033304