MATLAB Implementation of Kernel Fisher Discriminant Analysis

This kernel Fisher discriminant analysis code is implemented in MATLAB. In this implementation, we utilize the Fisher kernel for classification tasks. The Fisher kernel is a kernel function designed for nonlinear classification that projects data into a high-dimensional feature space where data points become more separable. The code implementation includes several key preprocessing steps: data normalization using z-score standardization and dimensionality reduction through Principal Component Analysis (PCA). The core algorithm implements the kernel trick by computing similarity matrices using Gaussian RBF kernels, followed by solving the generalized eigenvalue problem to find optimal projection directions. The classification module employs support vector machines (SVM) with regularization parameters to handle overfitting. Throughout the development process, we integrated essential machine learning techniques including k-fold cross-validation for hyperparameter tuning and L2 regularization to enhance model accuracy and generalization capability. The code structure follows modular design principles with separate functions for data preprocessing, kernel matrix computation, and classification evaluation.