Robust Mixture Discriminant Analysis

Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 , allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.


Reference manual

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1.2 by Charles Bouveyron, a year ago

Browse source code at

Authors: Charles Bouveyron & Stephane Girard

Documentation:   PDF Manual  

Task views: Robust Statistical Methods

GPL-2 license

Depends on MASS, mclust, Rsolnp

See at CRAN