Modular PCA Source Code Implementation

This documentation presents the MATLAB source code implementation of Modular PCA algorithm, demonstrated using the Yale Face Database. The implementation follows a systematic approach: first partitioning original images into blocks, then performing PCA analysis on all sub-images to extract projection features. For recognition images, the same partitioning process is applied, followed by calculating projection coefficients for each sub-image under the extracted features. Finally, classification is performed using a nearest neighbor classifier. The code structure includes key functions for image preprocessing, block partitioning, PCA transformation matrix calculation, and distance-based classification. PCA, as a fundamental statistical analysis method, helps uncover data structure patterns and enhances data analysis efficiency. The implementation allows further exploration of optimization techniques such as optimal feature selection and parameter tuning for the nearest neighbor classifier to improve image recognition accuracy. Key algorithmic components include: - Image block division with configurable block sizes - Efficient covariance matrix computation for high-dimensional sub-image data - Eigenvalue decomposition and feature vector selection - Projection coefficient calculation using matrix operations - Distance metric implementation for classification decisions The modular design enables easy adaptation to different datasets and classification requirements while maintaining computational efficiency through vectorized operations.