Hierarchical Probabilistic Models for Group Anomaly Detection
Liang Xiong, Barnabas Poczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas; JMLR W&CP 15:789-797, 2011.
AbstractStatistical anomaly detection typically focuses on finding individual data point anomalies. Often the most interesting or unusual things in a data set are not odd individual points, but rather larger scale phenomena that only become apparent when groups of data points are considered. In this paper, we propose two hierarchical probabilistic models for detecting such group anomalies. We evaluate our methods on synthetic data as well as astronomical data from the Sloan Digital Sky Survey. The experimental results show that the proposed models are effective in detecting group anomalies.