MATLAB Implementation of Fisher Linear Discriminant Analysis (FLDA) for Feature Dimensionality Reduction

This MATLAB implementation provides a comprehensive Fisher Linear Discriminant Analysis (FLDA) solution for multivariate data analysis tasks including feature dimensionality reduction, feature fusion, and correlation analysis. The code implementation helps users understand both the theoretical principles of Fisher Discriminant Analysis and its practical implementation through MATLAB programming. The implementation includes core functions for calculating between-class and within-class scatter matrices, eigenvalue decomposition for optimal projection direction determination, and dimensionality reduction transformations. Users can visualize data distributions before and after FLDA processing through integrated plotting functions, facilitating better data analysis and presentation outcomes. Key computational aspects covered include: covariance matrix computation, singular value decomposition handling, projection vector optimization, and class separability maximization algorithms. The code structure allows for easy modification of parameters and supports both two-class and multi-class classification scenarios with appropriate dataset formatting.