AUTHORS: Nian Zhang, Keenan Leatham
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ABSTRACT: In this paper, we propose a kernel based SVM algorithm with variable models to adapt to the high-dimensional but relatively small samples for remote explosive detection on photo-thermal infrared imaging spectroscopy (PT-IRIS) classification. The algorithms of the representative linear and nonlinear SVM are presented. The response plot, predicted vs. actual plot, and residuals plot of the linear, quadratic, and coarse Gaussian SVM are demonstrated. A comprehensive comparison of Linear SVM, Quadratic SVM, Cubic SVM, Fine Gaussian SVM, Median Gaussian SVM, Coarse Gaussian SVM is performed in terms of root mean square error, R-squared, mean squared error, and mean absolute error. The excellent experimental results demonstrated that the kernel based SVM models provide a promising solution to high-dimensional data sets with limited training samples.
KEYWORDS: Feature selection, Support vector machine, SVM, High-dimensional, Classification, Photothermal infrared imaging spectroscopy
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