As the amount of data generated and communicated continuously increases, clustering algorithms that are not able to handle this enormous amount of data have to be redesigned. Recent subspace clustering advances, while powerful, are computationally and memory demanding. The present paper introduces an online algorithm that broadens high-performance batch subspace clustering methods, and is able to perform subspace clustering on data arriving sequentially and possibly with misses. Numerical tests on synthetic and real data demonstrate the potential of the proposed approach.
|Original language||English (US)|
|Title of host publication||2016 50th Annual Conference on Information Systems and Sciences, CISS 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Apr 26 2016|
|Event||50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States|
Duration: Mar 16 2016 → Mar 18 2016
|Name||2016 50th Annual Conference on Information Systems and Sciences, CISS 2016|
|Other||50th Annual Conference on Information Systems and Sciences, CISS 2016|
|Period||3/16/16 → 3/18/16|
Bibliographical noteFunding Information:
Work was supported by NSF grants 1500713, 1514056; ARO W911NF-15-1-0492; and NIH 1R01GM104975-01.
- Low-rank representation
- Missing entries
- Subspace clustering