An indirect and efficient approach for solving uncorrelated optimal discriminant vectors

Authors:
Quan-Sen Sun;Zhong Jin;Pheng-Ann Heng;De-Shen Xia
Affiliations:
Department of Computer Science, Nanjing University of Science & Technology, Nanjing, China;Department of Computer Science, Nanjing University of Science & Technology, Nanjing, China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science, Nanjing University of Science & Technology, Nanjing, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

An approach for solving uncorrelated optimal discriminant vectors (UODV), called indirect uncorrelated linear discriminant analysis(IULDA), is proposed. This is done by establishing a relation between canonical correlation analysis (CCA) and Fisher linear discriminant analysis(FLDA). The advantages of our method for solving the UODV over the two existing methods are analyzed theoretically. Experimental result based on the Concordia University CENPARMI handwritten character database has shown that our algorithm can increase the recognition rate and the speed of feature extraction.