MATLAB M-File for Fisher Linear Discriminant Analysis with Gaussian Kernel Implementation

This is a MATLAB m-file implementing Fisher Linear Discriminant Analysis with source code utilizing Gaussian kernel transformation. Fisher Linear Discriminant Analysis is a fundamental method in pattern recognition and machine learning that identifies optimal separating hyperplanes in high-dimensional data. The implementation employs Gaussian kernel function to map data from original space to higher-dimensional feature space, where linear classification is performed. The code includes key computational components such as scatter matrix calculation, eigenvalue decomposition for projection vector derivation, and kernel trick implementation for nonlinear separation. Additionally, the m-file contains sample datasets and visualization utilities to demonstrate algorithm behavior and classification boundaries. For researchers and practitioners exploring Fisher discriminant analysis and its machine learning applications, this implementation serves as an excellent foundation. The modular code structure allows customization and extension to accommodate specific application requirements through parameter adjustments and kernel function modifications.