Principal Component Analysis: Applications and Limitations

This lecture covers the application of Principal Component Analysis (PCA) in reconstructing generative models, denoising, compression, and regression. It explores the limitations of PCA in preserving local neighborhood relationships and the importance of scaling data. The lecture also discusses alternative techniques like t-SNE for dimensionality reduction.