Unsupervised Learning Algorithms for Multimodal Pattern Classifiers

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.