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• W1965534165 abstract "Linear discriminant analysis (LDA) has been widely used for linear dimension reduction. However, LDA has limitations in that one of the scatter matrices is required to be nonsingular and the nonlinearly clustered structure is not easily captured. In order to overcome the problems caused by the singularity of the scatter matrices, a generalization of LDA based on the generalized singular value decomposition (GSVD) was recently developed. In this paper, we propose a nonlinear discriminant analysis based on the kernel method and the GSVD. The GSVD is applied to solve the generalized eigenvalue problem which is formulated in the feature space defined by a nonlinear mapping through kernel functions. Our GSVD-based kernel discriminant analysis is theoretically compared with other kernel-based nonlinear discriminant analysis algorithms. The experimental results show that our method is an effective nonlinear dimension reduction method." @default.
• W1965534165 created "2016-06-24" @default.
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• W1965534165 date "2005-01-01" @default.
• W1965534165 modified "2023-10-15" @default.
• W1965534165 title "Nonlinear Discriminant Analysis Using Kernel Functions and the Generalized Singular Value Decomposition" @default.
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• W1965534165 doi "https://doi.org/10.1137/s0895479804442334" @default.
• W1965534165 hasPublicationYear "2005" @default.
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