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Ruiz-Gazen, Anne; Marie-Sainte, Souad Larabi; Berro, Alain

Detecting multivariate outliers using projection pursuit with particle swarm optimization. (English) Zbl 1436.62306

Lechevallier, Yves (ed.) et al., Proceedings of COMPSTAT’2010. 19th international conference on computational statistics, Paris, France, August 22–27, 2010. Keynote, invited and contributed papers. Heidelberg: Physica Verlag. 89-98 (2010).
Summary: 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.
For the entire collection see [Zbl 1202.62001].

Cited in 1 Document

MSC:

62H99 Multivariate analysis
62H25 Factor analysis and principal components; correspondence analysis
62-08 Computational methods for problems pertaining to statistics
90C59 Approximation methods and heuristics in mathematical programming

Keywords:

heuristic algorithms; multivariate outliers detection; particle swarm optimization; projection pursuit; tribes algorithm
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References:

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