Subspace selection for anomaly detection: An information theory approach

Marcelo Bacher, Irad Ben-Gal, Erez Shmueli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel subspace selection algorithm for anomaly detection. Our method is based on the observation that it is easier to detect anomalies in subspaces comprise of highly correlative attributes. More specifically, it uses the Rokhlin metric [22] to evaluate the smallest information distance in the case of two attributes, and an extension of the Rokhlin distance in cases where more than two attributes are involved. In order to determine the set of subspaces to use, we apply a variation of the well known agglomerative clustering algorithm with the extended Rokhlin metric as the underlying distance function. An extensive evaluation that we conducted demonstrates that in most cases: (1) Our method outperforms state-of-the-art subspace selection algorithms for anomaly detection. (2) Our method yields significantly fewer subspaces (on average) than the other approaches, and (3) Our method does not require any tuning of parameters.

Original languageEnglish
Title of host publication2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509021529
DOIs
StatePublished - 4 Jan 2017
Event2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel
Duration: 16 Nov 201618 Nov 2016

Publication series

Name2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Conference

Conference2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/TerritoryIsrael
CityEilat
Period16/11/1618/11/16

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