Feature selection (2)
When the objective is to discriminate among objects, there is no guarantee that the most expressive features are necessarily good.
Optimality in discrimination among all possible linear combinations of features is achieved by employing linear discriminant analysis (LDA).
The feature vectors produced after the LDA projection are called most discriminating features.
PCA and LDA are parametric techniques closely related to matching pursuit filters.