Hierarchical Probabilistic Models for Group Anomaly Detection

Liang Xiong, Barnabás Póczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:789-797, 2011.

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 this Paper

BibTeX
@InProceedings{pmlr-v15-xiong11a, 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}, pages = {789--797}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/xiong11a/xiong11a.pdf}, url = {https://proceedings.mlr.press/v15/xiong11a.html}, 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.} }
Endnote
%0 Conference Paper %T Hierarchical Probabilistic Models for Group Anomaly Detection %A Liang Xiong %A Barnabás Póczos %A Jeff Schneider %A Andrew Connolly %A Jake VanderPlas %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-xiong11a %I PMLR %P 789--797 %U https://proceedings.mlr.press/v15/xiong11a.html %V 15 %X 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.
RIS
TY - CPAPER TI - Hierarchical Probabilistic Models for Group Anomaly Detection AU - Liang Xiong AU - Barnabás Póczos AU - Jeff Schneider AU - Andrew Connolly AU - Jake VanderPlas BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-xiong11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 789 EP - 797 L1 - http://proceedings.mlr.press/v15/xiong11a/xiong11a.pdf UR - https://proceedings.mlr.press/v15/xiong11a.html AB - 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. ER -
APA
Xiong, L., Póczos, B., Schneider, J., Connolly, A. & VanderPlas, J.. (2011). Hierarchical Probabilistic Models for Group Anomaly Detection. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:789-797 Available from https://proceedings.mlr.press/v15/xiong11a.html.

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