Title: Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies,
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.