Optimal kernel selection in kernel Fisher discriminant analysis (2006)
| Venue: | In Proceedings of the Twenty-Third International Conference on Machine Learning |
| Citations: | 18 - 1 self |
BibTeX
@INPROCEEDINGS{Kim06optimalkernel,
author = {Seung-jean Kim and Alessandro Magnani and Stephen Boyd},
title = {Optimal kernel selection in kernel Fisher discriminant analysis},
booktitle = {In Proceedings of the Twenty-Third International Conference on Machine Learning},
year = {2006},
pages = {465--472},
publisher = {ACM Press}
}
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Abstract
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. 1.
