Singular Value Decomposition (SVD): A matrix factorisation method that expresses a given matrix as the product of two orthogonal matrices and a diagonal matrix containing singular values.

Understanding Singular Value Decomposition If you have a matrix A with dim = n, it is possible to compute n eigenvalues (ordinary numbers like 1.234) and n associated eigenvectors, each with n values.

Understanding Singular Value Decomposition If you have a matrix A with dim = n, it is possible to compute n eigenvalues (ordinary numbers like 1.234) and n associated eigenvectors, each with n values.