  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.
