NDLI: Orthogonalized linear discriminant analysis based on modified generalized singular value decomposition

Content Provider	IEEE Xplore Digital Library
Author	Wei Wu Ahmad, M.O.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8 (Wei Wu; Ahmad, M.O.)
Abstract	Generalized singular value decomposition (GSVD) has been used in the literature for linear discriminant analysis (LDA) to solve the small sample size problem in pattern recognition. However, this algorithm suffers from excessive computational load when the sample dimension is high. In this paper, we present a modified version of the LDA/GSVD algorithm to enhance the computational efficiency, referred to as EGSVD-LDA algorithm, which uses the linear combination of the sample vectors to represent the singular vectors so as to circumvent the calculation of the high dimensional singular vectors through SVD. Further, to overcome the over-fitting problem of the GSVD-based algorithms, we have also proposed a new method to orthogonalize the discriminative subspace derived from the GSVD framework through a Gram-Schmidt process in an inner product space. These methods are efficient when data are high dimensional. Simulation results show that the EGSVD-LDA algorithm, especially its orthogonalized version, overcomes the computational complexity problem and provides high recognition accuracy with low computational load.
Starting Page	1629
Ending Page	1632
File Size	637316
Page Count	4
File Format	PDF
ISBN	9781424438273
DOI	10.1109/ISCAS.2009.5118084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear discriminant analysis Singular value decomposition Vectors Pattern recognition Computational complexity Scattering Computational modeling Matrix decomposition Computational efficiency Clustering algorithms
Content Type	Text
Resource Type	Article

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