Unsupervised Learning Algorithms for Multimodal Pattern Classifiers

Hiroyuki Matsunaga, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review


    Nearest neighbor pattern recognition is represented in terms of robust estimation, and a classification method using multimodal data fusion based on the Bayes rule is proposed. The proposed method is proved to be a kind of fuzzy voting. Unsupervised learning of classes' representative points using the EM algorithm is introduced. The basic properties of the proposed multimodal classifier are examined using simple data, and a qualitative explanation of the McGurk effect is offered. Experimental results on segmentation of multiple images are presented as an example of application.

    Original languageEnglish
    Pages (from-to)51-60
    Number of pages10
    JournalSystems and Computers in Japan
    Issue number8
    Publication statusPublished - Jan 1 1999

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Information Systems
    • Hardware and Architecture
    • Computational Theory and Mathematics


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