Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs

TL;DR

This paper introduces a non-parametric anomaly detection method for high-dimensional data that learns to approximate minimum volume sets using KNN graphs and max-margin ranking, achieving asymptotic optimality and computational efficiency.

Contribution

It presents a novel anomaly detection algorithm that combines KNN graph scores with max-margin learning to efficiently approximate density level sets with proven asymptotic optimality.

Findings

Outperforms existing KNN-based anomaly detection methods.

Achieves significant computational savings.

Demonstrates asymptotic optimality in detecting anomalies.

Abstract

We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on $n$-point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly at $\alpha$-false alarm level if the predicted score is in the $\alpha$-percentile. The resulting anomaly detector is shown to be asymptotically optimal in that for any false alarm rate $\alpha$, its decision region converges to the $\alpha$-percentile minimum volume level set of the unknown underlying density. In addition, we test both the statistical performance and computational efficiency of our algorithm on a number of synthetic and real-data experiments. Our results demonstrate the superiority of our algorithm over existing $K$-NN based anomaly detection algorithms, with significant computational savings.