Robust Novelty Detection with Single-Class MPM
Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet · Neural Information Processing Systems · 2002
In this paper we consider the problem of novelty detection, presenting an algorithm that aims to find a minimal region in input space containing a fraction α of the probability mass underlying a data set. This algorithm—the 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 a robust approach to estimating the mean and covariance matrix within the general two-class MPM setting, and show how this approach specializes to the single-class problem. We provide empirical results comparing the single-class MPM to the single-class SVM and a two-class SVM method.