Sciweavers

Share
ICML
2006
IEEE

Optimal kernel selection in Kernel Fisher discriminant analysis

11 years 8 days 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
Comments (0)
books