Linear Discriminant Analysis

Linear Discriminant Analysis (LDA) is a classic statistical method for classifying data by finding linear combinations of features that best separate different classes. Current research focuses on improving LDA's robustness and performance in challenging scenarios, such as high-dimensional data, missing values, and non-stationary data streams, through techniques like regularization, novel covariance matrix estimators, and adaptive algorithms. These advancements enhance LDA's accuracy and interpretability, making it a valuable tool for various applications, including those requiring explainable AI in fields like medicine and finance, as well as for efficient classification in resource-constrained environments.