  The main contribution of the paper is a new approach to subspace clustering
that is significantly more computationally efficient and scalable than existing
state-of-the-art methods. The central idea is to modify the regression
technique in sparse subspace clustering (SSC) by replacing the $\ell_1$
minimization with a generalization called Ordered Weighted $\ell_1$ (OWL)
minimization which performs simultaneous regression and clustering of
correlated variables. Using random geometric graph theory, we prove that OWL
regression selects more points within each subspace, resulting in better
clustering results. This allows for accurate subspace clustering based on
regression solutions for only a small subset of the total dataset,
significantly reducing the computational complexity compared to SSC. In
experiments, we find that our OWL approach can achieve a speedup of 20$\times$
to 30$\times$ for synthetic problems and 4$\times$ to 8$\times$ on real data
problems.
