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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1783.1/3179
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| Title: | Learning the kernel matrix by maximizing a KFD-based class separability criterion |
| Authors: | Yeung, Dit-Yan
Chang, Hong
Dai, Guang |
| Keywords: | Kernel learning
Fisher discriminant
Kernel Fisher discriminant
Face recognition |
| Issue Date: | Jul-2007 |
| Citation: | Pattern recognition, v. 40, no. 7, July 2007, p. 2021-2028 |
| Abstract: | The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this paper, we propose a novel method for learning the kernel matrix based on maximizing a class separability criterion that is similar to those used by linear discriminant analysis (LDA) and kernel Fisher discriminant (KFD). It is interesting to note that optimizing this criterion function does not require inverting the possibly singular within-class scatter matrix which is a computational problem encountered by many LDA and KFD methods. We have conducted experiments on both synthetic data and real-world data from UCI and FERET, showing that our method consistently outperforms some previous kernel learning methods. |
| Rights: | Pattern Recognition © copyright (2007) Elsevier. The Journal's web site is located at http://www.sciencedirect.com/ |
| URI: | http://hdl.handle.net/1783.1/3179 |
| Appears in Collections: | CSE Journal/Magazine Articles
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| yeung.pr2007b[1].pdf | pre-published version | 646Kb | Adobe PDF | View/Open |
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