### Abstract

Original language | English |
---|---|

Pages (from-to) | 1300-1304 |

Journal | IEEE Signal Processing Letters |

Volume | 26 |

Issue number | 9 |

Publication status | Published - 2019 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Artificial Intelligence
- Signal Processing

### Cite this

**Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification.** / Zollanvari, Amin; Abdirash, Muratkhan; Dadlani, Aresh; Abibullaev, Berdakh.

Research output: Contribution to journal › Article

*IEEE Signal Processing Letters*, vol. 26, no. 9, pp. 1300-1304.

}

TY - JOUR

T1 - Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis for Cost-Sensitive Binary Classification

AU - Zollanvari, Amin

AU - Abdirash, Muratkhan

AU - Dadlani, Aresh

AU - Abibullaev, Berdakh

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Regularized linear discriminant

KW - random matrix theory

KW - cost-sensitive classification

KW - bias correction

M3 - Article

VL - 26

SP - 1300

EP - 1304

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

SN - 1070-9908

IS - 9

ER -