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Covariance-enhanced discriminant analysis

Author

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. Sheng, Ying & Wang, Qihua, 2019. "Simultaneous variable selection and class fusion with penalized distance criterion based classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 138-152.
    2. Aaron J Molstad & Adam J Rothman, 2018. "Shrinking characteristics of precision matrix estimators," Biometrika, Biometrika Trust, vol. 105(3), pages 563-574.
    3. Kang, Xiaoning & Kang, Lulu & Chen, Wei & Deng, Xinwei, 2022. "A generative approach to modeling data with quantitative and qualitative responses," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    4. Briggs, Kristie, 2015. "Co-owner relationships conducive to high quality joint patents," Research Policy, Elsevier, vol. 44(8), pages 1566-1573.
    5. Pan, Yuqing & Mai, Qing, 2020. "Efficient computation for differential network analysis with applications to quadratic discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    6. Luo, Shan & Chen, Zehua, 2020. "A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification," Journal of Multivariate Analysis, Elsevier, vol. 179(C).

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