  Anomaly detection based on one-class classification algorithms is broadly
used in many applied domains like image processing (e.g. detection of whether a
patient is "cancerous" or "healthy" from mammography image), network intrusion
detection, etc. Performance of an anomaly detection algorithm crucially depends
on a kernel, used to measure similarity in a feature space. The standard
approaches (e.g. cross-validation) for kernel selection, used in two-class
classification problems, can not be used directly due to the specific nature of
a data (absence of a second, abnormal, class data). In this paper we generalize
several kernel selection methods from binary-class case to the case of
one-class classification and perform extensive comparison of these approaches
using both synthetic and real-world data.
