MATLAB Code Implementation for Principal Component Analysis (PCA)

The PCA program discussed in this article implements Principal Component Analysis, a statistical procedure that utilizes orthogonal transformation to convert potentially correlated variables into linearly uncorrelated principal components. This implementation features covariance matrix computation, eigenvalue decomposition using MATLAB's eig() function, and sorting of eigenvectors by descending eigenvalues to determine feature significance. The program efficiently reduces dataset dimensionality while preserving maximum variance, making it particularly valuable for pattern recognition and data visualization tasks. Key functions include data normalization, covariance calculation, and projection of original data onto principal component axes. Beyond this PCA implementation, we provide additional data analysis utilities featuring various algorithms for dataset preprocessing, clustering, and classification. These tools incorporate error handling and visualization components to facilitate comprehensive data understanding and processing workflows.