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Effective Sigma Selection for the Gaussian RBF Kernel

version 1.0.1 (1.84 KB) by Liu Zhiliang
A effective method for hyper-parameter selection of the Gaussian radial basis function kernel in the one-class classification problem.

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Updated 07 Nov 2019

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The kernel parameter σ is sensitive to the one-class classification model with the Gaussian RBF Kernel. This sigma selection method uses a line search with an state-of-the-art objective function to find the optimal value. The kernel matrix is the bridge between σ and the model. Instead of directly finding a good model, we aim to design a kernel matrix with preferred characteristics, which are reflected in the state-of-the-art objective function.

References:
[1] Zhiliang Liu*, Jinlong Kang, Xuejun Zhao, Ming J Zuo, Yong Qin, and Limin Jia. Modelling of the safe region based on support vector data description for health assessment of wheelset bearings. Applied Mathematical Modelling, 73: 19-39, 2019.

Cite As

Liu Zhiliang (2022). Effective Sigma Selection for the Gaussian RBF Kernel (https://www.mathworks.com/matlabcentral/fileexchange/73281-effective-sigma-selection-for-the-gaussian-rbf-kernel), MATLAB Central File Exchange. Retrieved .

Zhiliang Liu*, Jinlong Kang, Xuejun Zhao, Ming J Zuo, Yong Qin, and Limin Jia. Modelling of the safe region based on support vector data description for health assessment of wheelset bearings. Applied Mathematical Modelling, 73: 19-39, 2019.

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
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