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

    Fingerprint

    Hidden Markov models
    Set theory
    Program processors

    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

    Hidden Markov model estimation based on alpha-EM algorithm : Discrete and continuous alpha-HMMs. / Matsuyama, Yasuo.

    Proceedings of the International Joint Conference on Neural Networks. 2011. p. 808-816 6033304.

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

    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., 6033304, pp. 808-816, 2011 International Joint Conference on Neural Network, IJCNN 2011, San Jose, CA, 11/7/31. https://doi.org/10.1109/IJCNN.2011.6033304
    Matsuyama Y. Hidden Markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs. In Proceedings of the International Joint Conference on Neural Networks. 2011. p. 808-816. 6033304 https://doi.org/10.1109/IJCNN.2011.6033304
    Matsuyama, Yasuo. / Hidden Markov model estimation based on alpha-EM algorithm : Discrete and continuous alpha-HMMs. Proceedings of the International Joint Conference on Neural Networks. 2011. pp. 808-816
    @inproceedings{4129b794aeb7465ab1bd9cb38a1a40ce,
    title = "Hidden Markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs",
    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.",
    author = "Yasuo Matsuyama",
    year = "2011",
    doi = "10.1109/IJCNN.2011.6033304",
    language = "English",
    isbn = "9781457710865",
    pages = "808--816",
    booktitle = "Proceedings of the International Joint Conference on Neural Networks",

    }

    TY - GEN

    T1 - Hidden Markov model estimation based on alpha-EM algorithm

    T2 - Discrete and continuous alpha-HMMs

    AU - Matsuyama, Yasuo

    PY - 2011

    Y1 - 2011

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=80054730021&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=80054730021&partnerID=8YFLogxK

    U2 - 10.1109/IJCNN.2011.6033304

    DO - 10.1109/IJCNN.2011.6033304

    M3 - Conference contribution

    SN - 9781457710865

    SP - 808

    EP - 816

    BT - Proceedings of the International Joint Conference on Neural Networks

    ER -