Bayesian methods for low-rank matrix estimation: short survey and theoretical study
Machine Learning
2018-06-27 v2
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
@article{arxiv.1306.3862,
title = {Bayesian methods for low-rank matrix estimation: short survey and theoretical study},
author = {Pierre Alquier},
journal= {arXiv preprint arXiv:1306.3862},
year = {2018}
}
Comments
Corrected version of a paper published in the proceedings of ALT 2013