singular value decomposition

initialism: SVD
https://doi.org/10.1351/goldbook.10115
A factorization of an \(m \times n\) matrix (\(\boldsymbol{M}\)) such that \(\boldsymbol{M} = \boldsymbol{U}\boldsymbol{\Sigma} \boldsymbol{V}^{T}\), where \(\boldsymbol{U}\) is an \(m \times m\) matrix, \(\boldsymbol{\Sigma}\) is a \(m \times n\) matrix and \(\boldsymbol{V}^{\rm{T}}\) is a \(n \times n\) matrix.
Note: If \(\boldsymbol{M}\) is a data matrix with \(m\) objects and \(n\) variables, the matrix \(\boldsymbol{U}\) is the scores matrix, the diagonal of \(\boldsymbol{\Sigma}\) contain the square roots of the eigenvalues and \(\boldsymbol{V}\) is the loadings matrix.
Source:
PAC, 2016, 88, 407. 'Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)' on page 425 (https://doi.org/10.1515/pac-2015-0605)