Image Reconstruction Using PCA

Principal Component Analysis (PCA) is a powerful technique widely used in image processing, computer vision, and machine learning. It is a method for reducing the dimensionality of high-dimensional data while preserving the most important information, and it has many practical applications.

This project explores dimensionality reduction and image reconstruction using PCA.

The project focuses on understanding the mathematical concepts behind PCA, including eigenvectors, eigenvalues, and the decomposition of the covariance matrix. Using the scikit-learn library, PCA is applied to reduce the dimensionality of facial images, analyze the explained variance of the principal components, and reconstruct the original images using the PCA basis.

Additionally, the project addresses class imbalance through subsampling and visualizes the differences between original and reconstructed images to evaluate how effectively PCA captures essential features of the data.

By completing this project, you will gain a solid understanding of PCA, its practical applications in image processing, and how to interpret the results of dimensionality reduction and image reconstruction.