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) |
|---|---|
| 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 |
|---|---|
| Volume | 2017-December |
Conference
| Conference | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
|---|---|
| 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
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