Sparse Gaussian markov random field mixtures for anomaly detection

Tsuyoshi Idé, Ankush Khandelwal, Jayant Kalagnanam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

We propose a new approach to anomaly detection from multivariate noisy sensor data. We address two major challenges: To provide variable-wise diagnostic information and to automatically handle multiple operational modes. Our task is a practical extension of traditional outlier detection, which is to compute a single scalar for each sample. To consistently define the variable-wise anomaly score, we leverage a predictive conditional distribution. We then introduce a mixture of Gaussian Markov random field and its Bayesian inference, resulting in a sparse mixture of sparse graphical models. Our anomaly detection method is capable of automatically handling multiple operational modes while removing unwanted nuisance variables.We demonstrate the utility of our approach using real equipment data from the oil industry.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages955-960
Number of pages6
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jan 31 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume0
ISSN (Print)1550-4786

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
CountrySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

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