Anomaly detection in homogenous populations: A sparse multiple kernel-based regularization method

Tianshi Chen, Martin S. Andersen, Alessandro Chiuso, Gianluigi Pillonetto, Lennart Ljung

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

A problem of anomaly detection in homogenous populations consisting of linear stable systems is studied. The recently introduced sparse multiple kernel based regularization method is applied to solve the problem. A common problem with the existing regularization methods is that there lacks an efficient and systematic way to tune the involved regularization parameters. In contrast, the hyper-parameters (some of them can be interpreted as regularization parameters) involved in the proposed method are tuned in an automatic way, and in fact estimated by using the empirical Bayes method. What's more, both the parameter and hyper-parameter estimation problems can be cast as convex and sequential convex optimization problems. It is possible to derive scalable solutions to both the parameter and hyper-parameter estimation problems and thus provide a scalable solution to the anomaly detection.
Original languageEnglish
Title of host publicationProceedings of the 53rd IEEE Annual Conference on Decision and Control (CDC 2014)
PublisherIEEE
Publication date2014
Pages265-270
ISBN (Print)978-1-4799-7746-8
DOIs
Publication statusPublished - 2014
Event53rd IEEE Conference on Decision and Control (CDC 2014) - Los Angeles, United States
Duration: 15 Dec 201417 Dec 2014
http://control.disp.uniroma2.it/CDC2014/index.php

Conference

Conference53rd IEEE Conference on Decision and Control (CDC 2014)
CountryUnited States
CityLos Angeles
Period15/12/201417/12/2014
Internet address

Keywords

  • Communication, Networking and Broadcast Technologies

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