  We present a novel approach to the formulation and the resolution of sparse
Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal
Scoring. It has an exact equivalence with penalized LDA, contrary to the
multi-class approaches based on the regression of class indicator that have
been proposed so far. Sparsity is obtained thanks to a group-Lasso penalty that
selects the same features in all discriminant directions. Our experiments
demonstrate that this approach generates extremely parsimonious models without
compromising prediction performances. Besides prediction, the resulting sparse
discriminant directions are also amenable to low-dimensional representations of
data. Our algorithm is highly efficient for medium to large number of
variables, and is thus particularly well suited to the analysis of gene
expression data.

Machine Learning;Machine Learning