Name
Title | ||
|---|---|---|
Proximal Approaches for Matrix Optimization Problems: Application to Robust Precision Matrix Estimation |
Abstract | ||
|---|---|---|
•Proximal algorithms with sound convergence are proposed to solve matrix estimation problems.•New proximity operators for spectral functions within Bregman divergences are derived.•Graphical lasso problem under noisy environment is considered as illustrative example.•Novel majorization-Minimization algorithm is designed to solve the resulting nonconvex problem.•Numerical results show practical efficiency and robustness versus noise wrt state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.sigpro.2019.107417 | Signal Processing |
Keywords | Field | DocType |
Covariance estimation,Graphical lasso,Matrix optimization,Douglas–Rachford method,Majorization-minimization,Bregman divergence | Mathematical optimization,Estimation of covariance matrices,Matrix (mathematics),Lasso (statistics),Symmetric matrix,Bregman divergence,Covariance matrix,Optimization problem,Mathematics,Covariance | Journal |
Volume | ISSN | Citations |
169 | 0165-1684 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Benfenati | 1 | 6 | 2.13 |
| Emilie Chouzenoux | 2 | 202 | 26.37 |
| Jean-Christophe Pesquet | 3 | 18 | 11.52 |