A new reduced-rank linear discriminant analysis method and its applications

Research output: Contribution to journalArticle

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)189-202
Number of pages14
JournalStatistica Sinica
Volume28
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • Dimension reduction
  • Gene expression data
  • High-dimensional data
  • Multi-class classification
  • Supervised principal component analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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