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.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd
• Keywords: ICML 2006, Kernel Selection Method, Kernel Selection Problem, Machine Learning, Optimal Kernel