Main Approaches for Dimensionality Reduction
This content discusses dimensionality reduction approaches, focusing on projection and Manifold Learning. It explains how projection simplifies high-dimensional data, exemplified by datasets like the Swiss roll. Principal Component Analysis (PCA) is highlighted as a key algorithm for preserving variance while reducing dimensions, with SVD as a method for determining principal components.