Data Representation: PCA

This lecture covers the concepts of data representation, including Principal Component Analysis (PCA) for dimensionality reduction. It explains how PCA aims to keep important signal while removing noise, providing a mapping from high to low dimensions. The lecture also delves into the mathematical details of PCA, variance maximization, and the mapping process. Additionally, it discusses the limitations of linear methods like PCA and introduces nonlinear dimensionality reduction techniques like Kernel PCA.