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Covariance-enhanced discriminant analysis

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Listed:
  • Peirong Xu
  • Ji Zhu
  • Lixing Zhu
  • Yi Li

Abstract

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.

Suggested Citation

  • Peirong Xu & Ji Zhu & Lixing Zhu & Yi Li, 2015. "Covariance-enhanced discriminant analysis," Biometrika, Biometrika Trust, vol. 102(1), pages 33-45.
  • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:33-45.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu049
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    Cited by:

    1. repec:eee:csdana:v:133:y:2019:i:c:p:138-152 is not listed on IDEAS
    2. Briggs, Kristie, 2015. "Co-owner relationships conducive to high quality joint patents," Research Policy, Elsevier, vol. 44(8), pages 1566-1573.
    3. repec:oup:biomet:v:105:y:2018:i:3:p:563-574. is not listed on IDEAS

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