KPCA: Kernel Principal Component Analysis for Dimensionality Reduction
KPCA implementation featuring automatic dimensionality selection based on variance contribution rate, with kernel method integration for nonlinear data transformation
Reading Hyperspectral Remote Sensing Images and Performing Principal Component Analysis
This MATLAB-based program implements hyperspectral remote sensing image reading and principal component analysis, sorting and displaying results in descending order of contribution rate, with enhanced image processing capabilities.
MATLAB Program for Principal Component Analysis with Contribution Rate Output and 2D Scatter Plot Visualization
This MATLAB program implements Principal Component Analysis (PCA) with functionality to output component contribution rates and generate 2D scatter plots for data visualization in principal component space. The code efficiently performs data dimensionality reduction while preserving essential information through eigenvalue decomposition and covariance matrix computation.