Robust mixture models for anomaly detection

Oren Barkan, Amir Averbuch

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

Abstract

We propose robust density estimation in a low dimensional space for anomaly detection. The outline of the method is as follows: first a low dimensional representation of the original data is learnt. Then, a robust density mixture model is estimated in the learnt space. Finally, the likelihood of a data point given the model parameters is used to apply anomaly detection. An efficient way for adapting the model parameters when the data distribution is changing with time is proposed. We further show how to identify the actual parameters in the original feature space that accounts for the occurrence of the anomaly. We present experimental results that demonstrate the effectiveness of the proposed methods.

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - 8 Nov 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
Country/TerritoryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

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