Given a probability measure P and a reference measure µ, one is often interested in the minimum µ-measure set with P -measure at least α. Minimum volume sets of this type summarize the regions of greatest probability mass of P , and are useful for detecting anomalies and constructing confidence regions. This paper addresses the problem of estimating minimum volume sets based on independent samples distributed according to P . Other than these samples, no other information is available regarding P , but the reference measure µ is assumed to be known. We introduce rules for estimating minimum volume sets that parallel the empirical risk minimization and structural risk minimization principles in classification. As in classification, we show that the performances of our estimators are controlled by the rate of uniform convergence of empirical to true probabilities over the class from which the estimator is drawn. Thus we obtain finite sample size performance bounds in terms of VC dimension and related quantities. We also demonstrate strong universal consistency, an oracle inequality, and rates of convergence. The proposed estimators are illustrated with histogram and decision tree set estimation rules.
MVS corresponds to the region of greatest probability density with minimum data volume and is known to be useful for anomaly detection REF based on the assumption that anomalies occur in the less concentrated regions of the nominal dataset.
Learning Minimum Volume Sets
Journal of Machine Learning Research
Psychology Mathematics Computer Science