the SVD of an m×n real or complex matrix M where Where I is the identity matrix and where and are orthonormal given their constraints ( and ). Additionally, and are positive definite matrices (often diagonal matrices of weights). This form of the GSVD is the core of certain techniques, such as generalized principal component analysis and Correspondence analysis. The weighted form of the GSVD is called as such because, with the correct selection of weights, it generalizes many techniques (such as multidimensional scaling and linear discriminant analysis) References Further reading LAPACK manual Linear algebra Singular value decomposition