Primal-Dual Methods for Sparse Constrained Matrix Completion
Abstract
We develop scalable algorithms for regular and non-negative matrix completion. In particular, we base the methods on trace-norm regularization that induces a low rank predicted matrix. The regularization problem is solved via a constraint generation method that explicitly maintains a sparse dual and the corresponding low rank primal solution. We provide a new dual block coordinate descent algorithm for solving the dual problem with a few spectral constraints. Empirical results illustrate the effectiveness of our method in comparison to recently proposed alternatives.
Cite
Text
Xin and Jaakkola. "Primal-Dual Methods for Sparse Constrained Matrix Completion." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Xin and Jaakkola. "Primal-Dual Methods for Sparse Constrained Matrix Completion." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/xin2012aistats-primaldual/)BibTeX
@inproceedings{xin2012aistats-primaldual,
title = {{Primal-Dual Methods for Sparse Constrained Matrix Completion}},
author = {Xin, Yu and Jaakkola, Tommi},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {1323-1331},
volume = {22},
url = {https://mlanthology.org/aistats/2012/xin2012aistats-primaldual/}
}