An Overview of Robust Subspace Recovery
Machine Learning
2018-11-07 v2 Machine Learning
Abstract
This paper will serve as an introduction to the body of work on robust subspace recovery. Robust subspace recovery involves finding an underlying low-dimensional subspace in a dataset that is possibly corrupted with outliers. While this problem is easy to state, it has been difficult to develop optimal algorithms due to its underlying nonconvexity. This work emphasizes advantages and disadvantages of proposed approaches and unsolved problems in the area.
Keywords
Cite
@article{arxiv.1803.01013,
title = {An Overview of Robust Subspace Recovery},
author = {Gilad Lerman and Tyler Maunu},
journal= {arXiv preprint arXiv:1803.01013},
year = {2018}
}
Comments
31 pages, 5 figures, 3 tables