Big data clustering via random sketching and validation

Panagiotis A. Traganitis; Konstantinos Slavakis; Georgios B Giannakis

doi:10.1109/ACSSC.2014.7094614

Big data clustering via random sketching and validation

Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B Giannakis

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

2 Scopus citations

Abstract

As the number and dimensionality of data increases, development of new efficient processing tools has become a necessity. The present paper introduces a novel dimensionality reduction approach for fast and efficient clustering of high-dimensional data. The new methods extend random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to the dimensionality reduction problem. The advocated random sketching and validation K-means (SkeVa K-means) and Divergence SkeVa algorithms can achieve high performance, with the latter being able to afford lower computational footprint than the former. Extensive numerical tests on synthetic and real datasets highlight the potential of the proposed algorithms, and demonstrate their competitive performance relative to state-of-the-art random projection alternatives.

Original language	English (US)
Title of host publication	Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1046-1050
Number of pages	5
ISBN (Electronic)	9781479982974
DOIs	https://doi.org/10.1109/ACSSC.2014.7094614
State	Published - Apr 24 2015
Event	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 2 2014 → Nov 5 2014

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2015-April
ISSN (Print)	1058-6393

Other

Other	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/2/14 → 11/5/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

Clustering
K-means
big data
feature selection
high-dimensional data
random sampling and consensus
random sketching and validation

Publisher link

10.1109/ACSSC.2014.7094614

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Traganitis, P. A., Slavakis, K., & Giannakis, G. B. (2015). Big data clustering via random sketching and validation. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 1046-1050). Article 7094614 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094614

Big data clustering via random sketching and validation. / Traganitis, Panagiotis A.; Slavakis, Konstantinos; Giannakis, Georgios B.
Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. ed. / Michael B. Matthews. IEEE Computer Society, 2015. p. 1046-1050 7094614 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April).

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

Traganitis, PA, Slavakis, K & Giannakis, GB 2015, Big data clustering via random sketching and validation. in MB Matthews (ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers., 7094614, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2015-April, IEEE Computer Society, pp. 1046-1050, 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/2/14. https://doi.org/10.1109/ACSSC.2014.7094614

Traganitis, Panagiotis A. ; Slavakis, Konstantinos ; Giannakis, Georgios B. / Big data clustering via random sketching and validation. Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. editor / Michael B. Matthews. IEEE Computer Society, 2015. pp. 1046-1050 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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N2 - As the number and dimensionality of data increases, development of new efficient processing tools has become a necessity. The present paper introduces a novel dimensionality reduction approach for fast and efficient clustering of high-dimensional data. The new methods extend random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to the dimensionality reduction problem. The advocated random sketching and validation K-means (SkeVa K-means) and Divergence SkeVa algorithms can achieve high performance, with the latter being able to afford lower computational footprint than the former. Extensive numerical tests on synthetic and real datasets highlight the potential of the proposed algorithms, and demonstrate their competitive performance relative to state-of-the-art random projection alternatives.

AB - As the number and dimensionality of data increases, development of new efficient processing tools has become a necessity. The present paper introduces a novel dimensionality reduction approach for fast and efficient clustering of high-dimensional data. The new methods extend random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to the dimensionality reduction problem. The advocated random sketching and validation K-means (SkeVa K-means) and Divergence SkeVa algorithms can achieve high performance, with the latter being able to afford lower computational footprint than the former. Extensive numerical tests on synthetic and real datasets highlight the potential of the proposed algorithms, and demonstrate their competitive performance relative to state-of-the-art random projection alternatives.

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Big data clustering via random sketching and validation

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