Abstract
Kernel parameters optimization is one of the most challenging problems on kernel Fisher discriminant analysis (KFDA). In this paper, a simple and effective KFDA kernel parameters optimization criterion is proposed on the basis of the maximum margin criterion (MMC) that maximize the distances between any two classes. Actually, this MMC-based criterion is applied to the kernel parameters optimization on KFDA and KFDA with Locally Linear Embedding affinity matrix (KFDA-LLE). It is demonstrated by the experiments on six real-world multiclass datasets that, in comparison with two other criteria, our MMC-based criterion can detect the optimal KFDA kernel parameters more accurately in the cases of both RBF kernel and polynomial kernel.
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Zhao, Y., Ma, J. (2014). Kernel Parameter Optimization for KFDA Based on the Maximum Margin Criterion. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_37
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DOI: https://doi.org/10.1007/978-3-319-12436-0_37
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