The decomposition of A is called the singular value decomposition (SVD) and the diagonal elements of ∑ are the non-negative square roots of the eigenvalues of A T A; they are called singular values.Expand

The method of generalized cross-validation (GCV) for choosing a good value for λ from the data is studied, which can be used in subset selection and singular value truncation methods for regression, and even to choose from among mixtures of these methods.Expand

A large selection of solution methods for linear systems in saddle point form are presented, with an emphasis on iterative methods for large and sparse problems.Expand

The use of the pseudo-inverse $A^I = V\Sigma ^I U^* $ to solve least squares problems in a way which dampens spurious oscillation and cancellation is mentioned.Expand

Algorithms are presented which make extensive use of well-known reliable linear least squares techniques, and numerical results and comparisons are given.Expand

Imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process.Expand

An algorithm for solving the TLS problem is proposed that utilizes the singular value decomposition and which provides a measure of the underlying problem''s sensitivity.Expand

A new method for solving total variation (TV) minimization problems in image restoration by introducing an additional variable for the flux quantity appearing in the gradient of the objective function, which can be interpreted as the normal vector to the level sets of the image u.Expand