Explore MATLAB source code curated for "核主成分分析" with clean implementations, documentation, and examples.
MATLAB implementation of Kernel PCA using polynomial kernel function with detailed comments for clear understanding. This code demonstrates dimensionality reduction and data visualization techniques.
MATLAB source code implementation for Kernel Principal Component Analysis (KPCA) algorithm with kernel function integration and feature space transformation
To address PCA's limitation in processing nonlinear data for comprehensive multi-index analysis, we implemented the Kernel Principal Component Analysis (KPCA) method with Gaussian kernel to evaluate the quality characteristics of 16 distinct fermented bean curd varieties across different Chinese regions.
Source code implementation of KPCA (Kernel Principal Component Analysis) with comprehensive annotations and algorithm explanations for dimensionality reduction applications.
Kernel Principal Component Analysis using Support Vector Machines, practical implementation with complete code suitable for thesis projects and academic research
A detailed implementation code for Kernel PCA with algorithmic explanations and key function descriptions
A comprehensive MATLAB implementation of Kernel PCA with detailed code annotations and algorithm explanations