Fast Optimal Bandwidth Selection for RBF Kernel using Reproducing Kernel Hilbert Space Operators for Kernel Based Classifiers
This work addresses a computational bottleneck for researchers and practitioners using kernel-based methods in remote sensing classification, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of high computational complexity in selecting the bandwidth parameter for Gaussian kernels in kernel-based classifiers by proposing a fast method based on reproducing kernel Hilbert space operators. The result shows that this method outperforms state-of-the-art approaches in computational time and classification performance on hyperspectral datasets.
Kernel based methods have shown effective performance in many remote sensing classification tasks. However their performance significantly depend on its hyper-parameters. The conventional technique to estimate the parameter comes with high computational complexity. Thus, the objective of this letter is to propose an fast and efficient method to select the bandwidth parameter of the Gaussian kernel in the kernel based classification methods. The proposed method is developed based on the operators in the reproducing kernel Hilbert space and it is evaluated on Support vector machines and PerTurbo classification method. Experiments conducted with hyperspectral datasets show that our proposed method outperforms the state-of-art method in terms in computational time and classification performance.