Back To Index Previous Article Next Article Full Text

Statistica Sinica 28 (2018), 189-202

A NEW REDUCED-RANK LINEAR DISCRIMINANT
ANALYSIS METHOD AND ITS APPLICATIONS
Yue Selena Niu1 , Ning Hao1 and Bin Dong2
1University of Arizona and 2Peking University

Abstract: 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.

Key words and phrases: Dimension reduction, gene expression data, high-dimensional data, multi-class classification, supervised principal component analysis.

Back To Index Previous Article Next Article Full Text