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
- National Science Foundation [DMS 1309507]
- National Science Foundation of China [11671022]
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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