An Overview of Robust Subspace Recovery
It is an incremental overview for researchers in machine learning and data analysis, summarizing the field without presenting new methods or results.
The paper provides an introduction to robust subspace recovery, which aims to find low-dimensional subspaces in datasets with outliers, highlighting the challenges due to nonconvexity and discussing existing approaches and open problems.
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.