# Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator

arXiv: https://arxiv.org/abs/2401.17760 · HTML: https://mlsanity.com/paper/2401.17760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [A Large Dimensional Study of Regularized Discriminant Analysis Classifiers](https://mlsanity.com/paper/1711.00382/) (2017)
- [Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions](https://mlsanity.com/paper/1808.10188/) (2018)
- [Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants](https://mlsanity.com/paper/2004.08217/) (2020)
- [Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data](https://mlsanity.com/paper/1403.0904/) (2014)
- [Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes](https://mlsanity.com/paper/1509.07982/) (2015)
- [An Improved Modified Cholesky Decomposition Method for Precision Matrix Estimation](https://mlsanity.com/paper/1710.05163/) (2017)
- [Linear pooling of sample covariance matrices](https://mlsanity.com/paper/2008.05854/) (2020)
- [Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies](https://mlsanity.com/paper/1804.03981/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)

## Abstract (arXiv)

Linear discriminant analysis (LDA) is a widely used technique for data classification. The method offers adequate performance in many classification problems, but it becomes inefficient when the data covariance matrix is ill-conditioned. This often occurs when the feature space's dimensionality is higher than or comparable to the training data size. Regularized LDA (RLDA) methods based on regularized linear estimators of the data covariance matrix have been proposed to cope with such a situation. The performance of RLDA methods is well studied, with optimal regularization schemes already proposed. In this paper, we investigate the capability of a positive semidefinite ridge-type estimator of the inverse covariance matrix that coincides with a nonlinear (NL) covariance matrix estimator. The estimator is derived by reformulating the score function of the optimal classifier utilizing linear estimation methods, which eventually results in the proposed NL-RLDA classifier. We derive asymptotic and consistent estimators of the proposed technique's misclassification rate under the assumptions of a double-asymptotic regime and multivariate Gaussian model for the classes. The consistent estimator, coupled with a one-dimensional grid search, is used to set the value of the regularization parameter required for the proposed NL-RLDA classifier. Performance evaluations based on both synthetic and real data demonstrate the effectiveness of the proposed classifier. The proposed technique outperforms state-of-art methods over multiple datasets. When compared to state-of-the-art methods across various datasets, the proposed technique exhibits superior performance.