Weighted EM algorithm and block monitoring

Yasuo Matsuyama*

*Corresponding author for this work

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

    2 Citations (Scopus)


    The expectation and maximization algorithm (EM algorithm) is generalized so that the learning proceeds according to adjustable weights in terms of probability measures. The presented method, the weighted EM algorithm, or the α-EM algorithm includes the existing EM algorithm as a special case. It is further found that this learning structure can work systolically. It is also possible to add monitors to interact with lower systolic subsystems. This is made possible by attaching building blocks of the weighted (or plain) EM learning. Derivation of the whole algorithm is based on generalized divergences. In addition to the discussions on the learning, extensions of basic statistical properties such as Fisher's efficient score, his measure of information and Cramer-Rao's inequality are given. These appear in update equations of the generalized expectation learning. Experiments show that the presented generalized version contains cases that outperform traditional learning methods.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
    Place of PublicationPiscataway, NJ, United States
    Number of pages6
    Publication statusPublished - 1997
    EventProceedings of the 1997 IEEE International Conference on Neural Networks. Part 4 (of 4) - Houston, TX, USA
    Duration: 1997 Jun 91997 Jun 12


    OtherProceedings of the 1997 IEEE International Conference on Neural Networks. Part 4 (of 4)
    CityHouston, TX, USA

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Artificial Intelligence


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