  We consider multi-class classification problems for high dimensional data.
Following the idea of reduced-rank linear discriminant analysis (LDA), we
introduce a new dimension reduction tool with a flavor of supervised principal
component analysis (PCA). The proposed method is computationally efficient and
can incorporate the correlation structure among the features. Besides the
theoretical insights, we show that our method is a competitive classification
tool by simulated and real data examples.
