A randomized approach to large-scale subspace clustering

Panagiotis A. Traganitis; Georgios B Giannakis

doi:10.1109/ACSSC.2016.7869522

A randomized approach to large-scale subspace clustering

Panagiotis A. Traganitis, Georgios B Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

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
Pages	1019-1023
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869522
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

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

Bibliographical note

Funding Information:
Work in this paper was supported by NSF grants 1247885,1343248, 1343860, 1500713, 1514056, and NIH 1R01GM104975-01.

Publisher Copyright:
© 2016 IEEE.

Keywords

Subspace clustering
big data
random projections
sketching

Publisher link

10.1109/ACSSC.2016.7869522

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Traganitis, P. A., & Giannakis, G. B. (2017). A randomized approach to large-scale subspace clustering. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 1019-1023). Article 7869522 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869522

A randomized approach to large-scale subspace clustering. / Traganitis, Panagiotis A.; Giannakis, Georgios B.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. ed. / Michael B. Matthews. IEEE Computer Society, 2017. p. 1019-1023 7869522 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Traganitis, PA & Giannakis, GB 2017, A randomized approach to large-scale subspace clustering. in MB Matthews (ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869522, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 1019-1023, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869522

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abstract = "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.",

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A randomized approach to large-scale subspace clustering

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