Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm

Abstract

In this paper, we develop a novel approach to perform kernel parameter selection for Kernel Fisher discriminant analysis (KFDA) based on the viewpoint that optimal kernel parameter is associated with the maximum linear separability of samples in the feature space. This makes our approach for selecting kernel parameter of KFDA completely comply with the… (More)
DOI: 10.1007/978-3-540-74205-0_46

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Cite this paper

@inproceedings{Xu2007DetermineTK, title={Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm}, author={Yong Xu and Chuancai Liu and Chongyang Zhang}, booktitle={ICIC}, year={2007} }