An indirect and efficient approach for solving uncorrelated optimal discriminant vectors
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AbstractAn 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.
All Author(s) ListSun QS, Jin Z, Heng PA, Xia DS
Name of ConferenceInternational Conference on Intelligent Computing (ICIC)
Start Date of Conference16/08/2006
End Date of Conference19/08/2006
Place of ConferenceKunming
Journal nameLecture Notes in Artificial Intelligence
Volume Number4114
Pages1204 - 1209
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2021-10-05 at 02:47