Kernel PCA (KPCA) MATLAB Implementation with Detailed Code Annotation

This text introduces a MATLAB program implementing Kernel Principal Component Analysis (KPCA). While the code already contains detailed inline comments, we can further elaborate on its usage and technical aspects. The program is designed based on the KPCA algorithm, which specializes in dimensionality reduction for nonlinear datasets. For those unfamiliar with KPCA, here's a brief technical explanation: Unlike traditional Principal Component Analysis (PCA), KPCA handles nonlinear data structures by employing kernel functions to map input data into higher-dimensional feature spaces. The implementation includes key functions for kernel matrix computation (using Gaussian or polynomial kernels) and eigenvalue decomposition to extract nonlinear principal components. The code structure follows modular design with separate functions for data preprocessing, kernel parameter configuration, and projection visualization. Users can customize kernel parameters and visualize the transformed data in reduced dimensions. If you have any technical questions or improvement suggestions after downloading, please feel free to contact us for support.