Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study
Abstract
The problem of low-rank matrix estimation recently received a lot of attention due to challenging applications. A lot of work has been done on rank-penalized methods and convex relaxation, both on the theoretical and applied sides. However, only a few papers considered Bayesian estimation. In this paper, we review the different type of priors considered on matrices to favour low-rank. We also prove that the obtained Bayesian estimators, under suitable assumptions, enjoys the same optimality properties as the ones based on penalization.
Cite
Text
Alquier. "Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study." International Conference on Algorithmic Learning Theory, 2013. doi:10.1007/978-3-642-40935-6_22Markdown
[Alquier. "Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study." International Conference on Algorithmic Learning Theory, 2013.](https://mlanthology.org/alt/2013/alquier2013alt-bayesian/) doi:10.1007/978-3-642-40935-6_22BibTeX
@inproceedings{alquier2013alt-bayesian,
title = {{Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study}},
author = {Alquier, Pierre},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2013},
pages = {309-323},
doi = {10.1007/978-3-642-40935-6_22},
url = {https://mlanthology.org/alt/2013/alquier2013alt-bayesian/}
}