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Scalable anomaly detection in large homogeneous populations
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-8655-2655
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 5, 1459-1465 p.Article in journal (Refereed) Published
Abstract [en]

Anomaly detection in large populations is a challenging but highly relevant problem. It is essentially a multi-hypothesis problem, with a hypothesis for every division of the systems into normal and anomalous systems. The number of hypothesis grows rapidly with the number of systems and approximate solutions become a necessity for any problem of practical interest. In this paper we take an optimization approach to this multi-hypothesis problem. It is first shown to be equivalent to a non-convex combinatorial optimization problem and then is relaxed to a convex optimization problem that can be solved distributively on the systems and that stays computationally tractable as the number of systems increase. An interesting property of the proposed method is that it can under certain conditions be shown to give exactly the same result as the combinatorial multi-hypothesis problem and the relaxation is hence tight.

Place, publisher, year, edition, pages
International Federation of Automatic Control (IFAC) , 2014. Vol. 50, no 5, 1459-1465 p.
Keyword [en]
Anomaly detection; Outlier detection; Multi-hypothesis testing; Distributed optimization; System identification
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-108173DOI: 10.1016/j.automatica.2014.03.008ISI: 000336779100015OAI: oai:DiVA.org:liu-108173DiVA: diva2:729561
Available from: 2014-06-28 Created: 2014-06-26 Last updated: 2017-12-05

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