Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies
This addresses the issue of focusing on meaningful anomalies in data-driven detection, which is incremental as it builds on existing principles like Maximum Entropy Discrimination.
The paper tackles the problem of detecting anomalies with high real-world utility rather than all statistically rare instances, proposing LatLapMED to incorporate utility labels and showing superior performance over methods that pre-process with unsupervised detection.
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.