Abstract
While successful in clustering multiple types of high-dimensional data, subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering algorithm for high-dimensional data based on a random sketching and validation approach. Utilizing a data-driven random sketching technique to estimate the underlying probability density function of the data, the performance of the proposed method is assessed via simulations, and is compared with state-of-the-art sparse subspace clustering methods.
Original language | English (US) |
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Title of host publication | Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 107-111 |
Number of pages | 5 |
ISBN (Electronic) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2016-February |
ISSN (Print) | 1058-6393 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
Bibliographical note
Funding Information:Work in this paper was supported by NSF grants 1343248, 1343860, 1500713, 1514056, and NIH 1R01GM104975-01
Publisher Copyright:
© 2015 IEEE.
Keywords
- Subspace clustering
- big data
- kernel smoothing
- random sketching and validation
- sparsity