- The main paper is dense. This is despite the commendable efforts by the authors to make their contributions as readable as possible. I believe it is due to NIPS page limit restrictions; the same set of ideas presented at their natural length would make for a more easily digestible paper.
- The authors do not quite discuss computational aspects in detail (other than a short discussion in the appendix), but it is unclear whether their proposed methods can be made practically useful for high dimensions. As stated, their algorithm requires solving several LPs in high dimensions, each involving a parameter that is not easily calculable. This is reflected in the authorsâ experiments which are all performed on very small scale datasets.
- The authors mainly seem to focus on SSC, and do not contrast their method with several other subsequent methods (thresholded subspace clustering (TSC), greedy subspace clustering by Park, etc) which are all computationally efficient as well as come with similar guarantees.
- The authors mainly seem to focus on SSC, and do not contrast their method with several other subsequent methods (thresholded subspace clustering (TSC), greedy subspace clustering by Park, etc) which are all computationally efficient as well as come with similar guarantees.