What is 'SVD' (Singular Value Decomposition)?

What is 'SVD' (Singular Value Decomposition)?

Approach: Describe the factorization of a matrix into three specific matrices.

A matrix factorization that decomposes any matrix A into UΣV*, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values. It is used in PCA and noise reduction.

Follow-up: How does it relate to the Moore-Penrose pseudoinverse?

Key concepts: dimensionality reduction, eigenvectors, U Sigma V*.