Singular value decomposition (SVD)

Singular Value Decomposition (SVD) is a mathematical technique used in linear algebra to factorize a real or complex matrix into three matrices. It decomposes a matrix into a product of three other matrices, which are the left singular vectors, the diagonal matrix of singular values, and the right singular vectors. This decomposition is useful in many applications such as signal processing, statistics, and machine learning.