### Abstract

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 language | English |
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Pages (from-to) | 1300-1304 |

Journal | IEEE Signal Processing Letters |

Volume | 26 |

Issue number | 9 |

Publication status | Published - 2019 |

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### Keywords

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

### ASJC Scopus subject areas

- Artificial Intelligence
- Signal Processing