Pattern classification using composite features

Authors:
Chunghoon Kim;Chong-Ho Choi
Affiliations:
School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper, we propose a new classification method using composite features, each of which consists of a number of primitive features. The covariance of two composite features contains information on statistical dependency among multiple primitive features. A new discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods.