Reference : Regularized Discriminant Analysis in Presence of Cellwise Contamination
Scientific congresses and symposiums : Unpublished conference/Abstract
Physical, chemical, mathematical & earth Sciences : Mathematics
Regularized Discriminant Analysis in Presence of Cellwise Contamination
Aerts, Stéphanie mailto [Université de Liège > HEC Liège : UER > UER Opérations : Informatique de gestion >]
Wilms, Ines mailto []
Joint Statistical Meetings 2017
du 29 juillet au 3 août 2017
American Statistical Association
[en] Cellwise robust precision matrix ; Classification ; Discriminant analysis ; Penalized estimation
[en] Quadratic and Linear Discriminant Analysis (QDA/LDA) are the most often applied classification rules under normality. In QDA, a separate covariance matrix is estimated for each group. If there are more variables than observations in the groups, the usual estimates are singular and cannot be used anymore. Assuming homoscedasticity, as in LDA, reduces the number of parameters to estimate. This rather strong assumption is however rarely verified in practice. Regularized discriminant techniques that are computable in high-dimension and cover the path between the two extremes QDA and LDA have been proposed in the literature. However, these procedures rely on sample covariance matrices. As such, they become inappropriate in presence of cellwise outliers, a type of outliers that is very likely to occur in high-dimensional datasets. We propose cellwise robust counterparts of these regularized discriminant techniques by inserting cellwise robust covariance matrices. Our methodology results in a family of discriminant methods that are robust against outlying cells, cover the gap between LDA and QDA and are computable in high-dimension.
Researchers ; Professionals

File(s) associated to this reference

Fulltext file(s):

Open access
CRRDA.pdfPublisher postprint490.36 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.