Abstract

In this paper we consider the problem of novelty detection, presenting an algorithm that aims to nd a minimal region in input space containing a fraction of the probability mass underlying a data set. This algorithm -- the "single-class minimax probability machine (MPM)" -- is built on a distribution-free methodology that minimizes the worst-case probability of a data point falling outside of a convex set, given only the mean and covariance matrix of the distribution and making no further distributional assumptions. We present

Citations