Singular Value Decomposition

#Learning Objectives

• Implement SVD via power iteration and explain the geometric meaning of U, Sigma, and V^T
• Apply truncated SVD for image compression and measure the compression ratio vs reconstruction error
• Compute the Moore-Penrose pseudoinverse via SVD to solve overdetermined least-squares systems
• Connect SVD to PCA, recommendation systems (latent factors), and Latent Semantic Analysis in NLP