Work

An Overview of Robust Subspace Recovery

Year: 2018

Type: article

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.

Source: Proceedings of the IEEE

DOI: 10.1109/jproc.2018.2853141

Authors: Gilad Lerman, Tyler Maunu

Institution: University of Minnesota

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, University of Minnesota

Award: Novel Paradigms in Geometric Modeling of Large and High-Dimensional Data Sets