Optimal Kernel Selection in Kernel Fisher Discriminant Analysis
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.
Cite
Text
Kim et al. "Optimal Kernel Selection in Kernel Fisher Discriminant Analysis." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143903Markdown
[Kim et al. "Optimal Kernel Selection in Kernel Fisher Discriminant Analysis." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/kim2006icml-optimal/) doi:10.1145/1143844.1143903BibTeX
@inproceedings{kim2006icml-optimal,
title = {{Optimal Kernel Selection in Kernel Fisher Discriminant Analysis}},
author = {Kim, Seung-Jean and Magnani, Alessandro and Boyd, Stephen P.},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {465-472},
doi = {10.1145/1143844.1143903},
url = {https://mlanthology.org/icml/2006/kim2006icml-optimal/}
}