Unsupervised Learning Algorithms for Multimodal Pattern Classifiers

Hiroyuki Matsunaga, Kiichi Urahama

Research output: Contribution to journalArticle

Abstract

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
Volume30
Issue number8
DOIs
Publication statusPublished - Jan 1 1999

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Unsupervised learning
Unsupervised Learning
Data fusion
Learning algorithms
Pattern recognition
Learning Algorithm
Classifiers
Classifier
Bayes Rule
Robust Estimation
Data Fusion
EM Algorithm
Voting
Pattern Recognition
Nearest Neighbor
Segmentation
Experimental Results
Class

All Science Journal Classification (ASJC) codes

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

Cite this

Unsupervised Learning Algorithms for Multimodal Pattern Classifiers. / Matsunaga, Hiroyuki; Urahama, Kiichi.

In: Systems and Computers in Japan, Vol. 30, No. 8, 01.01.1999, p. 51-60.

Research output: Contribution to journalArticle

Matsunaga, Hiroyuki ; Urahama, Kiichi. / Unsupervised Learning Algorithms for Multimodal Pattern Classifiers. In: Systems and Computers in Japan. 1999 ; Vol. 30, No. 8. pp. 51-60.
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