Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies

TL;DR

This paper introduces LatLapMED, a novel anomaly detection method that combines geometric entropy minimization and maximum entropy discrimination to focus on high-utility anomalies, outperforming existing methods.

Contribution

The paper proposes LatLapMED, integrating EM, geometric entropy minimization, and maximum entropy discrimination to detect high-utility anomalies more effectively.

Findings

Superior performance over existing methods in simulated datasets

Effective in real-world anomaly detection scenarios

Combines utility labels with statistical anomaly detection

Abstract

Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have high real-world utility, while ignoring the low-utility statistically anomalous instances. To this end, we propose a novel method called Latent Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution. This method uses the EM algorithm to simultaneously incorporate the Geometric Entropy Minimization principle for identifying statistical anomalies, and the Maximum Entropy Discrimination principle to incorporate utility labels, in order to detect high-utility anomalies. We apply our method in both simulated and real datasets to demonstrate that it has superior performance over existing alternatives that independently pre-process with unsupervised anomaly detection algorithms before classifying.