From subspace clustering to full-rank matrix completion

@inproceedings{CandsFromSC,
  title={From subspace clustering to full-rank matrix completion},
  author={Emmanuel J. Cand{\`e}s and Lester Mackey and Mahdi Soltanolkotabi}
}
Subspace clustering is the problem of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This type of structure occurs naturally in many applications ranging from bioinformatics, image/text clustering to semi-supervised learning. The companion paper [3] shows that robust and tractable subspace clustering is possible with minimal requirements on the orientation of the subspaces and number of samples per subspace. This note… CONTINUE READING