Gaussian kernel optimization for pattern classification |
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Authors: | Jie Wang [Author Vitae] [Author Vitae] KN Plataniotis [Author Vitae] [Author Vitae] |
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Affiliation: | a Epson Edge, 3771 Victoria Park Avenue, Toronto, Canada M1W 3Z5 b The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Canada M5A 3G4 c Vidient Systems, Inc., 4000 Burton Dr., Santa Clara, CA 95054, USA |
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Abstract: | This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples in the kernel feature space. We propose to optimize the Gaussian kernel parameters by maximizing a classical class separability criterion, and the problem is solved through a quasi-Newton algorithm by making use of a recently proposed decomposition of the objective criterion. The proposed method is evaluated on five data sets with two kernel-based learning algorithms. The experimental results indicate that it achieves the best overall classification performance, compared with three competing solutions. In particular, the proposed method provides a valuable kernel optimization solution in the severe small sample size scenario. |
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Keywords: | Gaussian kernel Kernel optimization Pattern classification Small sample size |
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