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|Title:||Multivariate outlier detection with compositional data|
|Keywords:||ЭБ БГУ::ОБЩЕСТВЕННЫЕ НАУКИ::Информатика|
|Abstract:||Multivariate outlier detection is usually based on Mahalanobis distances, by plugging in robust estimates of location and covariance. For compositional data, carrying only relative information, a special transformation needs to be consulted in order to be able to work in the appropriate geometry. The e ect of the trans- formation is discussed in this contribution. Furthermore, di erent possibilities for the interpretation of the identi ed multivariate outliers are presented.|
|Appears in Collections:||PLENARY LECTURES|
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