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ICML
2006
IEEE

Optimal kernel selection in Kernel Fisher discriminant analysis

15 years 10 months ago
Optimal kernel selection in Kernel Fisher discriminant analysis
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance of KFDA depends on the choice of the kernel; in this paper, we consider the problem of finding the optimal kernel, over a given convex set of kernels. We show that this optimal kernel selection problem can be reformulated as a tractable convex optimization problem which interior-point methods can solve globally and efficiently. The kernel selection method is demonstrated with some UCI machine learning benchmark examples.
Seung-Jean Kim, Alessandro Magnani, Stephen P. Boy
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2006
Where ICML
Authors Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd
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