In linear algebra, the singular-value decomposition (SVD) is a factorization of a real or complex matrix. It is the generalization of the eigendecomposition of a positive semidefinite normal matrix (for example, a symmetric matrixwith positive eigenvalues) to any  matrix via an extension of the polar decomposition. It has many useful applications in signal processing and statistics.