Subspace clustering has become a popular tool for clustering high-dimensional non-linearly separable data. However, state-of-the-art subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering scheme for high-volume data based on random projections. Performance of the proposed method is assessed via numerical tests, and is compared with state-of-the-art subspace clustering and large-scale subspace clustering methods.
|Original language||English (US)|
|Title of host publication||Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Mar 1 2017|
|Event||50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States|
Duration: Nov 6 2016 → Nov 9 2016
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Other||50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016|
|Period||11/6/16 → 11/9/16|
Bibliographical noteFunding Information:
Work in this paper was supported by NSF grants 1247885,1343248, 1343860, 1500713, 1514056, and NIH 1R01GM104975-01.
© 2016 IEEE.
- Subspace clustering
- big data
- random projections