Abstract
Subspace clustering has been a successful tool for unsupervised classification of high-dimensional and generally 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 distributed subspace clustering scheme for high-volume data based on random projections. Additionally, the method can cope with corrupted data. Performance of the novel scheme is assessed via numerical tests, and is compared with state-of-the-art subspace clustering methods.
Original language | English (US) |
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Title of host publication | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781538612514 |
DOIs | |
State | Published - Mar 9 2018 |
Event | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao Duration: Dec 10 2017 → Dec 13 2017 |
Publication series
Name | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
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Volume | 2017-December |
Conference
Conference | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
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City | Curacao |
Period | 12/10/17 → 12/13/17 |
Bibliographical note
Funding Information:Work in this paper was supported by NSF grants 1500713 and 1514056.
Publisher Copyright:
© 2017 IEEE.
Keywords
- Subspace clustering
- big data
- distributed
- missing data
- random projections
- sketching