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| 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. |
| Rights: | © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| URI: | http://hdl.handle.net/1783.1/2486 |
| Appears in Collections: | CSE Conference Papers
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