MATLAB Implementation of Kernel PCA with Face Recognition Application

This source code provides a complete implementation of Kernel PCA (Principal Component Analysis) specifically designed for facial recognition applications. The implementation includes built-in support for ORL32 and YALE32 facial datasets, which serve as standardized benchmarks for evaluating the algorithm's performance. The code demonstrates key computational steps including kernel matrix calculation, eigenvalue decomposition, and feature space transformation using various kernel functions (RBF, polynomial, etc.). Through this implementation, users can study how Kernel PCA handles nonlinear dimensionality reduction by mapping input data to higher-dimensional feature spaces. The modular code structure allows for easy adaptation to other data types beyond facial images, providing practical insights into kernel method applications. The implementation includes data preprocessing routines, kernel parameter configuration, and projection visualization modules to facilitate comprehensive understanding. This resource serves as an excellent educational tool for mastering nonlinear feature extraction techniques, with well-commented code that explains critical algorithmic steps and optimization considerations. Users are encouraged to experiment with different kernel parameters and datasets to enhance their understanding of pattern recognition methodologies.