A novel distribution classifier

We present a novel classifier for a collection of nonnegative L1 functions. Given two sets of data, one set coming from “similar” distributions labeled as normal, and the other unspecified labeled as abnormal. To understand the structure of normality, and further to classify new data with minimal errors, we propose to find the smallest CKL spheres (based on Csiszar divergences) including as many normal data as possible and excluding as many abnormal data as possible. We prove the existence and uniqueness of such a classifier.