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ICML
2004
IEEE

A fast iterative algorithm for fisher discriminant using heterogeneous kernels

10 years 11 months ago
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a kernel function, we incorporate the task of choosing an appropriate kernel into the optimization problem to be solved. The choice of kernel is defined as a linear combination of kernels belonging to a potentially large family of different positive semidefinite kernels. The complexity of our algorithm does not increase significantly with respect to the number of kernels on the kernel family. Experiments on several benchmark datasets demonstrate that generalization performance of the proposed algorithm is not significantly different from that achieved by the standard KFD in which the kernel parameters have been tuned using cross validation. We also present results on a real-life colon cancer dataset that demonstrate the efficiency of the proposed method.
Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao
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