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Integrative linear discriminant analysis with guaranteed error rate improvement

Author

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  • Quefeng Li
  • Lexin Li

Abstract

SummaryMultiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer’s disease.

Suggested Citation

  • Quefeng Li & Lexin Li, 2018. "Integrative linear discriminant analysis with guaranteed error rate improvement," Biometrika, Biometrika Trust, vol. 105(4), pages 917-930.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:4:p:917-930.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy047
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    Cited by:

    1. 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).
    2. Sandra E. Safo & Eun Jeong Min & Lillian Haine, 2022. "Sparse linear discriminant analysis for multiview structured data," Biometrics, The International Biometric Society, vol. 78(2), pages 612-623, June.

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