PCA Principal Component Analysis Algorithm with MATLAB Implementation

In this article, we demonstrate how to implement the PCA (Principal Component Analysis) algorithm using MATLAB and provide supplementary materials to enhance your understanding of this technique. Beyond the core MATLAB source code for PCA, we detail essential data preprocessing steps including normalization and standardization procedures, along with visualization techniques for analyzing results. The implementation covers key MATLAB functions such as cov() for covariance matrix calculation and eig() for eigenvalue decomposition. We explain how to interpret principal components through variance analysis and component loading examination. Additionally, we discuss practical limitations and constraints of PCA in real-world applications, such as sensitivity to scaling and linearity assumptions, and provide guidance on selecting optimal approaches under these constraints. The article concludes with recommended references and resources for further exploration of principal component analysis and its applications in data science, including dimensionality reduction and feature extraction scenarios.