MATLAB Code for Principal Component Analysis (PCA)
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Detailed Documentation
This document provides a detailed MATLAB code implementation for Principal Component Analysis (PCA). The example demonstrates key PCA techniques including principal component extraction, variance contribution rate computation, and visualization through contribution rate histograms. The implementation utilizes MATLAB's built-in functions like pca() for eigenvalue decomposition and covariance matrix processing, with explicit calculations for cumulative variance percentages. Additional annotations and explanatory comments are included to enhance code readability and practical application. These implementation details will help users better understand PCA concepts and effectively apply them to research projects. The code structure follows standard PCA workflow: data standardization, covariance matrix computation, eigenvalue decomposition, component selection based on variance thresholds, and result visualization.
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