| Reference : Low-rank optimization for distance matrix completion |
| Scientific congresses and symposiums : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/2268/112432 | |||
| Low-rank optimization for distance matrix completion | |
| English | |
Mishra, Bamdev [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >] | |
Meyer, Gilles [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >] | |
Sepulchre, Rodolphe [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >] | |
| Dec-2011 | |
| Proceedings of the 50th IEEE Conference on Decision and Control | |
| Yes | |
| International | |
| 50th IEEE Conference on Decision and Control | |
| from 12-12-2011 to 15-12-2011 | |
| [en] low-rank ; distance matrix ; matrix completion | |
| [en] This paper addresses the problem of low-rank
distance matrix completion. This problem amounts to recover the missing entries of a distance matrix when the dimension of the data embedding space is possibly unknown but small compared to the number of considered data points. The focus is on high-dimensional problems. We recast the considered problem into an optimization problem over the set of low-rank positive semidefinite matrices and propose two efficient algorithms for low-rank distance matrix completion. In addition, we propose a strategy to determine the dimension of the embedding space. The resulting algorithms scale to high-dimensional problems and monotonically converge to a global solution of the problem. Finally, numerical experiments illustrate the good performance of the proposed algorithms on benchmarks. | |
| Researchers ; Professionals ; Students | |
| http://hdl.handle.net/2268/112432 |
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