KPCA Core Concept: Mapping Data from Input Space to High-Dimensional Feature Space

The fundamental idea of KPCA is to map data from input space to high-dimensional feature space and then compute principal components using linear PCA in the transformed space. This program provides KPCA source code with practical implementation, featuring kernel matrix computation and eigenvalue decomposition. Beginners can benefit from examining the complete workflow including data normalization, kernel function selection, and dimensionality reduction techniques.