Efficient subspace clustering of large-scale data streams with misses

Panagiotis A. Traganitis; Georgios B Giannakis

doi:10.1109/CISS.2016.7460569

Efficient subspace clustering of large-scale data streams with misses

Panagiotis A. Traganitis, Georgios B Giannakis

Electrical and Computer Engineering

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

3 Scopus citations

Abstract

As the amount of data generated and communicated continuously increases, clustering algorithms that are not able to handle this enormous amount of data have to be redesigned. Recent subspace clustering advances, while powerful, are computationally and memory demanding. The present paper introduces an online algorithm that broadens high-performance batch subspace clustering methods, and is able to perform subspace clustering on data arriving sequentially and possibly with misses. Numerical tests on synthetic and real data demonstrate the potential of the proposed approach.

Original language	English (US)
Title of host publication	2016 50th Annual Conference on Information Systems and Sciences, CISS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	590-595
Number of pages	6
ISBN (Electronic)	9781467394574
DOIs	https://doi.org/10.1109/CISS.2016.7460569
State	Published - Apr 26 2016
Event	50th Annual Conference on Information Systems and Sciences, CISS 2016 - Princeton, United States Duration: Mar 16 2016 → Mar 18 2016

Publication series

Name	2016 50th Annual Conference on Information Systems and Sciences, CISS 2016

Other

Other	50th Annual Conference on Information Systems and Sciences, CISS 2016
Country/Territory	United States
City	Princeton
Period	3/16/16 → 3/18/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Low-rank representation
Missing entries
Online
Streaming
Subspace clustering

Publisher link

10.1109/CISS.2016.7460569

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Traganitis, P. A., & Giannakis, G. B. (2016). Efficient subspace clustering of large-scale data streams with misses. In 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016 (pp. 590-595). Article 7460569 (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2016.7460569

Efficient subspace clustering of large-scale data streams with misses. / Traganitis, Panagiotis A.; Giannakis, Georgios B.
2016 50th Annual Conference on Information Systems and Sciences, CISS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 590-595 7460569 (2016 50th Annual Conference on Information Systems and Sciences, CISS 2016).

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

Traganitis, PA & Giannakis, GB 2016, Efficient subspace clustering of large-scale data streams with misses. in 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016., 7460569, 2016 50th Annual Conference on Information Systems and Sciences, CISS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 590-595, 50th Annual Conference on Information Systems and Sciences, CISS 2016, Princeton, United States, 3/16/16. https://doi.org/10.1109/CISS.2016.7460569

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AB - As the amount of data generated and communicated continuously increases, clustering algorithms that are not able to handle this enormous amount of data have to be redesigned. Recent subspace clustering advances, while powerful, are computationally and memory demanding. The present paper introduces an online algorithm that broadens high-performance batch subspace clustering methods, and is able to perform subspace clustering on data arriving sequentially and possibly with misses. Numerical tests on synthetic and real data demonstrate the potential of the proposed approach.

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ER -

Efficient subspace clustering of large-scale data streams with misses

Abstract

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Other

Bibliographical note

Keywords

Publisher link

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