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Global Multidisciplinary Journal

Open Access Peer Review International
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SPARSE REPRESENTATION TECHNIQUES FOR MULTIVARIATE EXTREMES: ANOMALY DETECTION APPLICATIONS

DOI https://doi.org/10.55640/gmj-abc112
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Nicolas Clémençon 1 , Stephan Sabourin 1 ,
4 LTCI, Télécom ParisTech, University of Paris-Saclay 46, rue Barrault, 75013, Paris, France
4 LTCI, Télécom ParisTech, University of Paris-Saclay 46, rue Barrault, 75013, Paris, France

Abstract

This study explores sparse representation techniques tailored for multivariate extremes and their application in anomaly detection. Multivariate extremes, characterized by rare events occurring jointly across multiple dimensions, pose significant challenges for traditional anomaly detection methods. Sparse representation approaches offer a promising solution by identifying a parsimonious set of extreme features that capture the most salient aspects of multivariate outliers. Leveraging techniques such as sparse coding, dictionary learning, and compressed sensing, sparse representation methods enable efficient representation and detection of anomalies in high-dimensional datasets. This paper reviews recent advances in sparse representation techniques for multivariate extremes and discusses their practical applications in anomaly detection across various domains, including finance, cybersecurity, and environmental monitoring.

 

Keywords

Sparse representation, multivariate extremes, anomaly detection

References

📄 C. Aggarwal, P.S. Yu, Outlier detection for high dimensional data, SIGMOD REC 30 (2001) 37–46.
📄 V. Barnett, T. Lewis, Outliers in Statistical Data, Wiley, New York, 1994.
📄 J. Beirlant, M. Escobar-Bach, Y. Goegebeur, A. Guillou, Bias-corrected estimation of stable tail dependence function, J. Multivariate Anal. 143 (2016) 453–466.
📄 J. Beirlant, Y. Goegebeur, J. Segers, J. Teugels, Statistics of Extremes: Theory and Applications, Wiley, Chichester, 2006.
📄 J. Beirlant, P. Vynckier, J.L. Teugels, Tail index estimation, Pareto quantile plots regression diagnostics, J. Amer. Statist. Assoc. 91 (1996) 1659–1667.
📄 M. Breunig, H. Kriegel, R. Ng, J. Sander, LOF: identifying density-based local outliers, SIGMOD Rec. 29 (2000) 93–104.
📄 V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: A survey, ACM Comput. Surv. (2009).
📄 D.A. Clifton, S. Hugueny, L. Tarassenko, Novelty detection with multivariate extreme value statistics, J. Signal Process. Syst. 65 (2011) 371–389.
📄 D.A. Clifton, L. Tarassenko, N. McGrogan, D. King, S. King, P. Anuzis, Bayesian extreme value statistics for novelty detection in gas-turbine engines, Aerosp. Conf. Proc. (2008) 1–11.
📄 S. Coles, J. Bawa, L. Trenner, P. and Dorazio, An Introduction to Statistical Modeling of Extreme Values, Springer, New York, 2001.
📄 S. Coles, J.A. Tawn, Modeling extreme multivariate events, J. R. Stat. Soc. Ser. B Stat. Methodol. 53 (1991) 377–392.
📄 D. Cooley, R. Davis, P. Naveau, The pairwise beta distribution: A flexible parametric multivariate model for extremes, J. Multivariate Anal. 101 (2010) 2103–2117.

How to Cite

Nicolas Clémençon, & Stephan Sabourin. (2023). SPARSE REPRESENTATION TECHNIQUES FOR MULTIVARIATE EXTREMES: ANOMALY DETECTION APPLICATIONS. Global Multidisciplinary Journal, 2(01), 01-07. https://doi.org/10.55640/gmj-abc112

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