MATLAB Code Implementation of Discriminant Analysis

This document provides MATLAB source code implementations for performing discriminant analysis. The repository includes three distinct methodologies: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and k-Nearest Neighbors (k-NN) discriminant analysis. Each algorithm implementation comes with dedicated test datasets to facilitate comprehensive testing and validation. Discriminant analysis serves as a powerful statistical method for classification and prediction tasks, with extensive applications in machine learning and data mining domains. The LDA implementation utilizes covariance matrix estimation and eigenvalue decomposition for dimensionality reduction, while QDA employs separate covariance matrices for each class to handle non-linear decision boundaries. The k-NN classifier incorporates distance metrics and voting mechanisms for neighborhood-based classification. We anticipate these code implementations will enhance your understanding and practical application of discriminant analysis algorithms, while providing valuable reference material for research and professional projects.