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- W1489734615 abstract "This paper develops a supervised dimensionality reduction method, Lorentzian Discriminant Projection (LDP), for discriminant analysis and classification. Our method represents the structures of sample data by a manifold, which is furnished with a Lorentzian metric tensor. Different from classic discriminant analysis techniques, LDP uses distances from points to their within-class neighbors and global geometric centroid to model a new manifold to detect the intrinsic local and global geometric structures of data set. In this way, both the geometry of a group of classes and global data structures can be learnt from the Lorentzian metric tensor. Thus discriminant analysis in the original sample space reduces to metric learning on a Lorentzian manifold. The experimental results on benchmark databases demonstrate the effectiveness of our proposed method." @default.
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- W1489734615 date "2010-01-01" @default.
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- W1489734615 title "Lorentzian Discriminant Projection and Its Applications" @default.
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- W1489734615 doi "https://doi.org/10.1007/978-3-642-12297-2_30" @default.
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