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.
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Feng, G., Hu, D., Li, M., Zhou, Z. (2005). A Novel LDA Approach for High-Dimensional Data. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_23
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DOI: https://doi.org/10.1007/11539087_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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