Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification

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In this letter, the theory of random matrices of increasing dimension is used to construct a form of Regularized Linear Discriminant Analysis (RLDA) that asymptotically yields the lowest overall risk with respect to the bias of the discriminant in cost-sensitive classification of two multivariate Gaussian distributions. Numerical experiments using both synthetic and real data show that even in finite-sample settings, the proposed classifier can uniformly outperform RLDA in terms of achieving a lower risk as a function of regularization parameter and misclassification costs.
Original languageEnglish
Pages (from-to)1300-1304
JournalIEEE Signal Processing Letters
Issue number9
Publication statusPublished - 2019



  • Regularized linear discriminant
  • random matrix theory
  • cost-sensitive classification
  • bias correction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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