Covariance-enhanced discriminant analysis

Peirong Xu, Ji Zhu, Lixing ZHU, Yi Li

Research output: Contribution to journalJournal articlepeer-review

14 Citations (Scopus)


Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial.

Original languageEnglish
Pages (from-to)33-45
Number of pages13
Issue number1
Publication statusPublished - 1 Mar 2015

Scopus Subject Areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

User-Defined Keywords

  • Correlation
  • Graphical lasso
  • Linear discriminant analysis
  • Pairwise fusion
  • Variable selection


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