4.3 Article

A NEW REDUCED-RANK LINEAR DISCRIMINANT ANALYSIS METHOD AND ITS APPLICATIONS

期刊

STATISTICA SINICA
卷 28, 期 1, 页码 189-202

出版社

STATISTICA SINICA
DOI: 10.5705/ss.202015.0387

关键词

Dimension reduction; gene expression data; high-dimensional data; multi-class classification; supervised principal component analysis

资金

  1. National Science Foundation [DMS 1309507]
  2. National Science Foundation of China [11671022]

向作者/读者索取更多资源

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.

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