An Overview of Robust Subspace Recovery

Gilad Lerman; Tyler Maunu

doi:10.1109/JPROC.2018.2853141

An Overview of Robust Subspace Recovery

Gilad Lerman, Tyler Maunu

Mathematics

Research output: Contribution to journal › Review article › peer-review

78 Scopus citations

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 data set 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.

Original language	English (US)
Article number	8425657
Pages (from-to)	1380-1410
Number of pages	31
Journal	Proceedings of the IEEE
Volume	106
Issue number	8
DOIs	https://doi.org/10.1109/JPROC.2018.2853141
State	Published - Aug 2018

Bibliographical note

Publisher Copyright:
© 1963-2012 IEEE.

Keywords

Big data
Dimension reduction
Nonconvex optimization
Recovery guarantees
Robustness
Subspace modeling
Unsupervised learning

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An Overview of Robust Subspace Recovery

Abstract

Bibliographical note

Keywords

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