# An improved robust algorithms for fisher discriminant model with high dimensional data

## TL;DR

This paper introduces a robust Fisher discriminant method for high-dimensional data that is less affected by outliers.

## Contribution

The novel integration of the MRCD algorithm into the Fisher discriminant framework improves robustness and accuracy in high-dimensional settings.

## Key findings

- The MRCD-Fisher discriminant outperforms existing methods in terms of robustness and accuracy.
- It maintains high data cleanliness and computational stability even with outlier-contaminated data.

## Abstract

This paper presents an improved robust Fisher discriminant method designed to handle high-dimensional data, particularly in the presence of outliers. Traditional Fisher discriminant methods are sensitive to outliers, which can significantly degrade their performance. To address this issue, we integrate the Minimum Regularized Covariance Determinant (MRCD) algorithm into the Fisher discriminant framework, resulting in the MRCD-Fisher discriminant model. The MRCD algorithm enhances robustness by regularizing the covariance matrix, making it suitable for high-dimensional data where the number of variables exceeds the number of observations. We conduct comparative experiments with other robust discriminant methods, the results demonstrate that the MRCD-Fisher discriminant outperforms these methods in terms of robustness and accuracy, especially when dealing with data contaminated by outliers. The MRCD-Fisher discriminant maintains high data cleanliness and computational stability, making it a reliable choice for high-dimensional data analysis. This study provides a valuable contribution to the field of robust statistical analysis, offering a practical solution for handling complex, outlier-prone datasets.