Kernel Choice in One-Class Support Vector Machines for Novelty and Outlier Detection

Novelty and outlier detection are both used for anomaly detection. This paper works through the method of One-Class support vector machine (SVM) which could estimate the contour of initial observations and can be applied in the problems of anomaly detection. In this paper, the experiments on both artificial and real-world data sets are performed to demonstrate the importance of kernel and kernel parameter choice and the corresponding sensitivity of the algorithm. Due to the desire for general regulations of kernel parameter selection, some general methods of selecting the kernel bandwidth parameter of RBF kernel are therefore investigated, including median heuristic method and Bayesian kernel learning method. Then, the experiments based on these methods are conducted to observe their effect. Consequently, some new discoveries together with current issues on these methods, as well as some applicable situations of these methods, are found in this paper. Furthermore, this paper also confirms the effectiveness of RBF kernel and that the initial value of its bandwidth parameter can be set by those two methods.