Random matrix improved subspace clustering

Type
Conference Paper

Authors
Couillet, Romain
Kammoun, Abla

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2017-03-06

Print Publication Date
2016-11

Date
2017-03-06

Abstract
This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.

Citation
Couillet R, Kammoun A (2016) Random matrix improved subspace clustering. 2016 50th Asilomar Conference on Signals, Systems and Computers. Available: http://dx.doi.org/10.1109/ACSSC.2016.7869000.

Acknowledgements
Couillet’s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).

Publisher
IEEE

Journal
2016 50th Asilomar Conference on Signals, Systems and Computers

Conference/Event Name
50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016

DOI
10.1109/ACSSC.2016.7869000

Additional Links
http://ieeexplore.ieee.org/document/7869000/https://hal.archives-ouvertes.fr/hal-01633444/file/couillet_UEclustering_asilomar.pdf

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