Quadratic Discriminant Analysis

Quadratic Discriminant Analysis (QDA) is a statistical classification technique that models data using quadratic decision boundaries, offering greater flexibility than linear methods for datasets with non-linear class separation. Current research focuses on improving QDA's robustness to non-Gaussian data and unequal covariance matrices, exploring modifications like risk-based calibration and the development of novel algorithms such as FEMDA for handling heterogeneous distributions. These advancements enhance QDA's applicability in diverse fields, including healthcare (e.g., disease prediction), engineering design (e.g., concept space analysis), and computer vision (e.g., person re-identification), by improving classification accuracy and generalizability.