Add to ORKGIn this thesis a method for the partially norm constrained least squares problem is presented. The method relies on a large-scale trust-region solver and has a low storage requirement. A combination of image misalignment and the inverse problem deblurring illustrates the use of the method.Master i InformatikkMAMN-INFINF
We study norms that can be used as penalties in machine learning problems. In particular, we conside...
Recent advances in total least squares approaches for solving various errors-in-variables modeling p...
The majority of methods for sparse precision matrix estimation rely on computationally expensive pro...
In this thesis a method for the partially norm constrained least squares problem is presented. The m...
Ill-posed problems arise in many areas of science and engineering. Their solutions, if they exist, a...
The aim of variable selection is the identification of the most important predictors that define the...
This paper addresses the problem of selecting the regularization parameter for linear least-squares ...
This is a collection of information about regularized least squares (RLS). The facts here are not ne...
International audienceThis paper studies least-square regression penalized with partly smooth convex...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
Under the Gaussian assumption, the relationship between conditional independence and sparsity allows...
We discuss the solution of numerically ill-posed overdetermined systems of equations using Tikhonov ...
The need to solve discrete ill-posed problems arises in many areas of science and engineering. Solut...
open4noopenLombardi, Michele; Baldo, Federico; Borghesi, Andrea; Milano, MichelaLombardi, Michele; B...
AbstractWe consider the least-squares problem minx∈Rn ‖Kx − y‖2, where K is ill-conditioned and y co...
We study norms that can be used as penalties in machine learning problems. In particular, we conside...
Recent advances in total least squares approaches for solving various errors-in-variables modeling p...
The majority of methods for sparse precision matrix estimation rely on computationally expensive pro...
In this thesis a method for the partially norm constrained least squares problem is presented. The m...
Ill-posed problems arise in many areas of science and engineering. Their solutions, if they exist, a...
The aim of variable selection is the identification of the most important predictors that define the...
This paper addresses the problem of selecting the regularization parameter for linear least-squares ...
This is a collection of information about regularized least squares (RLS). The facts here are not ne...
International audienceThis paper studies least-square regression penalized with partly smooth convex...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
Under the Gaussian assumption, the relationship between conditional independence and sparsity allows...
We discuss the solution of numerically ill-posed overdetermined systems of equations using Tikhonov ...
The need to solve discrete ill-posed problems arises in many areas of science and engineering. Solut...
open4noopenLombardi, Michele; Baldo, Federico; Borghesi, Andrea; Milano, MichelaLombardi, Michele; B...
AbstractWe consider the least-squares problem minx∈Rn ‖Kx − y‖2, where K is ill-conditioned and y co...
We study norms that can be used as penalties in machine learning problems. In particular, we conside...
Recent advances in total least squares approaches for solving various errors-in-variables modeling p...
The majority of methods for sparse precision matrix estimation rely on computationally expensive pro...