Robust discriminant analysis

TL;DR

This paper reviews robust discriminant analysis methods that improve classification reliability by addressing outliers and mislabeled data, highlighting techniques based on robust estimates and visualization tools.

Contribution

It provides an overview of robust DA techniques, emphasizing the use of robust estimates and diagnostic tools to enhance classification robustness.

Findings

Standard DA is sensitive to outliers and mislabeled data.

Robust DA methods improve reliability in contaminated data scenarios.

Graphical diagnostic tools aid in interpreting robust DA results.

Abstract

Discriminant analysis (DA) is one of the most popular methods for classification due to its conceptual simplicity, low computational cost, and often solid performance. In its standard form, DA uses the arithmetic mean and sample covariance matrix to estimate the center and scatter of each class. We discuss and illustrate how this makes standard DA very sensitive to suspicious data points, such as outliers and mislabeled cases. We then present an overview of techniques for robust DA, which are more reliable in the presence of deviating cases. In particular, we review DA based on robust estimates of location and scatter, along with graphical diagnostic tools for visualizing the results of DA.