Skip to Main content Skip to Navigation

Variable selection in model-based discriminant analysis

Abstract : A general methodology for selecting predictors for Gaussian generative classification models is presented. The problem is regarded as a model selection problem. Three different roles for each possible predictor are considered: a variable can be a relevant classification predictor or not, and the irrelevant classification variables can be linearly dependent on a part of the relevant predictors or independent variables. This variable selection model was inspired by the model-based clustering model of Maugis, Celeux and Martin-Magniette (2009) in a previous work on variable selection in model-based clustering. A BIC-like model selection criterion is proposed. It is optimized through two embedded forward stepwise variable selection algorithms for classification and linear regression. The model identifiability and the consistency of the variable selection criterion are proved. Numerical experiments on simulated and real data sets illustrate the interest of this variable selection methodology. In particular, it is shown that this well ground variable selection model can be of great interest to improve the classification performance of the quadratic discriminant analysis in a high dimension context
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download
Contributor : Gilles Celeux Connect in order to contact the contributor
Submitted on : Wednesday, May 12, 2010 - 6:50:24 PM
Last modification on : Friday, August 5, 2022 - 2:38:10 PM
Long-term archiving on: : Thursday, September 16, 2010 - 2:42:19 PM


Files produced by the author(s)


  • HAL Id : inria-00483229, version 1


Cathy Maugis, Gilles Celeux, Marie-Laure Martin-Magniette. Variable selection in model-based discriminant analysis. [Research Report] RR-7290, INRIA. 2010. ⟨inria-00483229⟩



Record views


Files downloads