Real-time discriminant analysis in the presence of label and measurement noise

TL;DR

This paper introduces a real-time robust quadratic discriminant analysis method that effectively handles label and measurement noise, incorporates anomaly detection, and visualizes data contamination, demonstrated on large-scale and real datasets.

Contribution

It proposes a novel real-time robust QDA approach with integrated anomaly detection and a new visualization tool for data noise identification.

Findings

Effective noise handling in large datasets

Improved classification accuracy with robust estimators

Visualization tool aids in detecting data contamination

Abstract

Quadratic discriminant analysis (QDA) is a widely used classification technique. Based on a training dataset, each class in the data is characterized by an estimate of its center and shape, which can then be used to assign unseen observations to one of the classes. The traditional QDA rule relies on the empirical mean and covariance matrix. Unfortunately, these estimators are sensitive to label and measurement noise which often impairs the model's predictive ability. Robust estimators of location and scatter are resistant to this type of contamination. However, they have a prohibitive computational cost for large scale industrial experiments. We present a novel QDA method based on a recent real-time robust algorithm. We additionally integrate an anomaly detection step to classify the most atypical observations into a separate class of outliers. Finally, we introduce the label bias plot, a graphical display to identify label and measurement noise in the training data. The performance of the proposed approach is illustrated in a simulation study with huge datasets, and on real datasets about diabetes and fruit.