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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