PCA Principal Component Analysis Implementation in MATLAB

This document presents an implementation of Principal Component Analysis (PCA) method using MATLAB programming. PCA serves as a powerful technique in image processing applications, particularly for feature extraction and dimensionality reduction tasks. The MATLAB code provided demonstrates the algorithmic workflow of PCA, including key computational steps such as covariance matrix calculation, eigenvalue decomposition, and principal component transformation. The implementation follows standardized PCA procedures where the algorithm first centers the data by subtracting the mean, computes the covariance matrix of the normalized data, performs eigenvalue decomposition to identify principal components, and finally projects the data onto the new feature space. The code contains detailed annotations that explain each computational stage, making it particularly valuable for understanding how PCA reduces dimensionality while preserving maximum variance in image data. For researchers and developers working with image processing, this resource provides both theoretical insights and practical implementation guidance, demonstrating how to effectively apply PCA for optimizing feature representation and reducing computational complexity in image analysis workflows.