robustDA: Robust Mixture Discriminant Analysis
Description.
Robust mixture discriminant analysis 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.
An application to object recognition under weak
supervision is presented below.
Participants.
Publication.
C. Bouveyron & S. Girard. "Robust supervised classification with mixture models: Learning from data with uncertain labels",
Pattern Recognition, 42(11), 2649--2658, 2009.
[Associated technical report: pdf].
Download.
A toolbox for R is available here.
