A New Reduced-Rank Linear Discriminant Analysis Method and Its Applications
Yue Niu, Ning Hao, Bin Dong · Statistica Sinica · 2017
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.