Name
Abstract | ||
|---|---|---|
This paper presents an algorithm that solves optimization problems on a matrix manifold M⊆Rm×n with an additional rank inequality constraint. The algorithm resorts to well-known Riemannian optimization schemes on fixed-rank manifolds, combined with new mechanisms to increase or decrease the rank. The convergence of the algorithm is analyzed and a weighted low-rank approximation problem is used to illustrate the efficiency and effectiveness of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2016.02.030 | Neurocomputing |
Keywords | Field | DocType |
Low-rank optimization,Rank-constrained optimization,Riemannian manifold,Fixed-rank manifold,Low-rank approximation | Convergence (routing),Applied mathematics,Riemannian manifold,Adaptive method,Matrix (mathematics),Riemannian optimization,Artificial intelligence,Optimization problem,Manifold,Combinatorics,Pattern recognition,Low-rank approximation,Mathematics | Journal |
Volume | ISSN | Citations |
192 | 0925-2312 | 2 |
PageRank | References | Authors |
0.36 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guifang Zhou | 1 | 2 | 0.36 |
| Wen Huang | 2 | 77 | 8.07 |
| Kyle Gallivan | 3 | 889 | 154.22 |
| Paul van Dooren | 4 | 649 | 90.48 |
| Pierre-Antoine Absil | 5 | 348 | 34.17 |