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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2486
Title: Nonlinear dimensionality reduction for classification using kernel weighted subspace method
Authors: Dai, Guang
Yeung, Dit-Yan
Keywords: Kernel subspace methods
Kernel weighted nonlinear discriminant analysis (KWNDA)
Nonlinear dimensionality reduction
Issue Date: 2005
Citation: Proceedings IEEE International Conference on Image Processing, 2005. ICIP 2005. Genoa, Italy, 11-14 September 2005. vol. 2, p. 838-841
Abstract: We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weighted nonlinear discriminant analysis (KWNDA) which possesses several appealing properties. First, like all kernel methods, it handles nonlinearity in a disciplined manner that is also computationally attractive. Second, by introducing weighting functions into the discriminant criterion, it outperforms existing kernel discriminant analysis methods in terms of the classification accuracy. Moreover, it also effectively deals with the small sample size problem. We empirically compare different subspace methods with respect to their classification performance of facial images based on the simple nearest neighbor rule. Experimental results show that KWNDA substantially outperforms competing linear as well as nonlinear subspace methods.
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URI: http://hdl.handle.net/1783.1/2486
Appears in Collections:CSE Conference Papers

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