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Detecting Multivariate Outliers Using Projection Pursuit with Particle Swarm Optimization

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Proceedings of COMPSTAT'2010

Abstract

Detecting outliers in the context of multivariate data is known as an important but difficult task and there already exist several detection methods. Most of the proposed methods are based either on the Mahalanobis distance of the observations to the center of the distribution or on a projection pursuit (PP) approach. In the present paper we focus on the one-dimensional PP approach which may be of particular interest when the data are not elliptically symmetric. We give a survey of the statistical literature on PP for multivariate outliers etection and investigate the pros and cons of the different methods. We also propose the use of a recent heuristic optimization algorithm called Tribes for multivariate outliers detection in the projection pursuit context.

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Author information

Authors and Affiliations

  1. Toulouse School of Economics (Gremaq et IMT), 21, allée de Brienne, 31000, Toulouse, France

    Anne Ruiz-Gazen

  2. IRIT, 21, allée de Brienne, 31000, Toulouse, France

    Souad Larabi Marie-Sainte & Alain Berro

Authors
  1. Anne Ruiz-Gazen
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  2. Souad Larabi Marie-Sainte
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  3. Alain Berro
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Corresponding author

Correspondence to Anne Ruiz-Gazen .

Editor information

Editors and Affiliations

  1. Centre de Recherche INRIA Paris-Rocquenc, Domaine de Voluceau, Le Chesnay cedex, 78153, France

    Yves Lechevallier

  2. , chaire de statistique appliquée, CNAM, rue Saint Martin 292, Paris, 75003, France

    Gilbert Saporta

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Ruiz-Gazen, A., Marie-Sainte, S.L., Berro, A. (2010). Detecting Multivariate Outliers Using Projection Pursuit with Particle Swarm Optimization. In: Lechevallier, Y., Saporta, G. (eds) Proceedings of COMPSTAT'2010. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2604-3_8

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