Brief

This article explains spectral decomposition, a mathematical technique used in machine learning to simplify matrices. It breaks down a matrix into its fundamental components: directions (eigenvectors) and their corresponding strengths (eigenvalues). The text details three primary types of spectral decomposition: Eigen decomposition for square matrices, the Spectral Theorem for symmetric matrices, and Singular Value Decomposition (SVD) which is a more general method applicable to any matrix, including rectangular ones. AI