Fast estimation of Hidden Markov Models via alpha-EM algorithm

Yasuo Matsuyama, Ryunosuke Hayashi, Ryota Yokote

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

    Abstract

    Fast estimation algorithms of Hidden Markov Models (HMMs), or alpha-HMMs, are presented. Such novel algorithms inherit speedup properties of the alpha-EM algorithm. Since the alpha-EM algorithm includes the traditional log-EM algorithm as its special case, the alpha-HMM also includes the traditional log-HMM as its special case. This generalization appears as the utilization of the past information which is the main device of the speedup. Since the memorization of the past information requires only little increase of computational load and memory, the iteration speedup directly appears as that of CPU time. Experimental results are given.

    Original languageEnglish
    Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
    Pages89-92
    Number of pages4
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice
    Duration: 2011 Jun 282011 Jun 30

    Other

    Other2011 IEEE Statistical Signal Processing Workshop, SSP 2011
    CityNice
    Period11/6/2811/6/30

    Keywords

    • alpha-EM algorithm
    • alpha-HMM
    • past information
    • speedup

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Applied Mathematics
    • Signal Processing
    • Computer Science Applications

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  • Cite this

    Matsuyama, Y., Hayashi, R., & Yokote, R. (2011). Fast estimation of Hidden Markov Models via alpha-EM algorithm. In IEEE Workshop on Statistical Signal Processing Proceedings (pp. 89-92). [5967835] https://doi.org/10.1109/SSP.2011.5967835