A novel LDA approach for high-dimensional data

Authors:
Guiyu Feng;Dewen Hu;Ming Li;Zongtan Zhou
Affiliations:
Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Linear Discriminant Analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of this method is that it may encounter the small sample size problem in practice. In this paper, we present a novel LDA approach for high-dimensional data. Instead of direct dimension reduction using PCA as the first step, the high-dimensional data are mapped into a relatively lower dimensional similarity space, and then the LDA technique is applied. The preliminary experimental results on the ORL face database verify the effectiveness of the proposed approach.