165 results about "Singular value decomposition" patented technology

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 matrix with non-negative eigenvalues) to any m×n matrix via an extension of the polar decomposition. It has many useful applications in signal processing and statistics. Formally, the singular value decomposition of an m×n real or complex matrix 𝐌 is a factorization of the form 𝐔𝚺𝐕*, where 𝐔 is an m×m real or complex unitary matrix, 𝚺 is an m×n rectangular diagonal matrix with non-negative real numbers on the diagonal, and 𝐕 is an n×n real or complex unitary matrix.