Low-Rank Matrix Completion by Riemannian Optimization

@article{Vandereycken2013LowRankMC,
  title={Low-Rank Matrix Completion by Riemannian Optimization},
  author={Bart Vandereycken},
  journal={SIAM Journal on Optimization},
  year={2013},
  volume={23},
  pages={1214-1236}
}
The matrix completion problem consists of finding or approximating a low-rank matrix based on a few samples of this matrix. We propose a novel algorithm for matrix completion that minimizes the least square distance on the sampling set over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical non-linear conjugate gradients, developed within the framework of retraction-based optimization on manifolds. We describe all the necessary objects from differential… CONTINUE READING
Highly Influential
This paper has highly influenced 30 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 220 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 141 extracted citations

220 Citations

0204060'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 220 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…