PCA-Based Feature Extraction Source Code

This document describes a feature extraction technique called Principal Component Analysis (PCA), a widely-used data dimensionality reduction method. PCA performs linear transformations to project high-dimensional data into lower-dimensional spaces. The algorithm calculates new feature vectors formed by combinations of original data features, enabling more efficient data representation that enhances analytical accuracy and processing efficiency. The accompanying MATLAB source code implements the complete PCA workflow, featuring: - Covariance matrix computation and eigenvalue decomposition - Principal component sorting based on variance contribution rates - Data projection onto principal component subspaces - Optional standardization preprocessing for numerical stability Key functions include eigenvalue sorting algorithms and orthogonal transformation implementations that ensure dimensional independence. The code demonstrates practical applications for dataset feature extraction, showing how to reduce computational complexity while preserving critical data patterns. As a fundamental technique with broad applications in data analysis and machine learning, PCA provides effective solutions for handling high-dimensional datasets through intelligent feature compression and reconstruction mechanisms.