Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction |
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Authors: | AK Qin PN Suganthan M Loog |
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Affiliation: | a School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 b The Image Group IT University of Copenhagen, Rued Langgaards Vej 7, 2300 Copenhagen S, Denmark |
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Abstract: | We propose a generalized null space uncorrelated Fisher discriminant analysis (GNUFDA) technique integrating the uncorrelated discriminant analysis and weighted pairwise Fisher criterion. The GNUFDA can effectively deal with the small sample-size problem and perform satisfactorily when the dimensionality of the null space decreases with increase in the number of training samples per class and/or classes, C. The proposed GNUFDA can extract at most C-1 optimal uncorrelated discriminative vectors without being influenced by the null-space dimensionality. |
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Keywords: | Null space of the within-class scatter matrix Uncorrelated Fisher discriminant analysis Weighted pairwise Fisher criterion |
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