MATLAB Source Code Implementation of PCA Algorithm

In this article, we share the MATLAB source code for the PCA algorithm that we developed collaboratively with classmates. During the programming process, we encountered several challenges including data preprocessing, covariance matrix calculation, and eigenvalue decomposition implementation, but ultimately successfully completed a functional PCA implementation. Our code implements key PCA components: data standardization using z-score normalization, covariance matrix computation via MATLAB's built-in functions, eigenvalue decomposition using eig() function, and projection of data onto principal components. We believe sharing this source code can help others learn and understand the practical implementation process of the PCA algorithm. If you're interested in dimensionality reduction techniques, you can experiment with our program and provide feedback or suggestions. We're enthusiastic about offering support and assistance to the learning community. Thank you for your interest!