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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Sun, QS., Jin, Z., Heng, PA., Xia, DS. (2006). An Indirect and Efficient Approach for Solving Uncorrelated Optimal Discriminant Vectors. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_149
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DOI: https://doi.org/10.1007/978-3-540-37275-2_149
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
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