Hierarchical Probabilistic Models for Group Anomaly Detection
Abstract
Statistical 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.
Cite
Text
Xiong et al. "Hierarchical Probabilistic Models for Group Anomaly Detection." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Xiong et al. "Hierarchical Probabilistic Models for Group Anomaly Detection." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/xiong2011aistats-hierarchical/)BibTeX
@inproceedings{xiong2011aistats-hierarchical,
title = {{Hierarchical Probabilistic Models for Group Anomaly Detection}},
author = {Xiong, Liang and Póczos, Barnabás and Schneider, Jeff and Connolly, Andrew and VanderPlas, Jake},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {789-797},
volume = {15},
url = {https://mlanthology.org/aistats/2011/xiong2011aistats-hierarchical/}
}