Title: The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description,
Abstract: Support vector data description (SVDD) is a popular technique for detecting
anomalies. The SVDD classifier partitions the whole space into an inlier
region, which consists of the region near the training data, and an outlier
region, which consists of points away from the training data. The computation
of the SVDD classifier requires a kernel function, and the Gaussian kernel is a
common choice for the kernel function. The Gaussian kernel has a bandwidth
parameter, whose value is important for good results. A small bandwidth leads
to overfitting, and the resulting SVDD classifier overestimates the number of
anomalies. A large bandwidth leads to underfitting, and the classifier fails to
detect many anomalies. In this paper we present a new automatic, unsupervised
method for selecting the Gaussian kernel bandwidth. The selected value can be
computed quickly, and it is competitive with existing bandwidth selection
methods.