This project explores dimensionality reduction techniques through a comparative analysis of Principal Component Analysis (PCA) and Gaussian Process Latent Variable Models (GPLVM).
The methods were applied both to a simulated cylindrical dataset and to the MNIST handwritten digits dataset to evaluate and compare their effectiveness in capturing low-dimensional structures within high-dimensional data.
The work represents my bachelor’s thesis project, focused on assessing the strengths and limitations of linear (PCA) and nonlinear (GPLVM) approaches for feature extraction and visualization.