  We revisit the problem of feature selection in linear discriminant analysis
(LDA), that is, when features are correlated. First, we introduce a pooled
centroids formulation of the multiclass LDA predictor function, in which the
relative weights of Mahalanobis-transformed predictors are given by
correlation-adjusted $t$-scores (cat scores). Second, for feature selection we
propose thresholding cat scores by controlling false nondiscovery rates (FNDR).
Third, training of the classifier is based on James--Stein shrinkage estimates
of correlations and variances, where regularization parameters are chosen
analytically without resampling. Overall, this results in an effective and
computationally inexpensive framework for high-dimensional prediction with
natural feature selection. The proposed shrinkage discriminant procedures are
implemented in the R package ``sda'' available from the R repository CRAN.
