A Consistent Method for Graph Based Anomaly Localization


Satoshi Hara, Tetsuro Morimura, Toshihiro Takahashi, Hiroki Yanagisawa, Taiji Suzuki ;
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:333-341, 2015.


The anomaly localization task aims at detecting faulty sensors automatically by monitoring the sensor values. In this paper, we propose an anomaly localization algorithm with a consistency guarantee on its results. Although several algorithms were proposed in the last decade, the consistency of the localization results was not discussed in the literature. To the best of our knowledge, this is the first study that provides theoretical guarantees for the localization results. Our new approach is to formulate the task as solving the sparsest subgraph problem on a difference graph. Since this problem is NP-hard, we then use a convex quadratic programming approximation algorithm, which is guaranteed to be consistent under suitable conditions. Across the simulations on both synthetic and real world datasets, we verify that the proposed method achieves higher anomaly localization performance compared to existing methods.

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