Random matrix improved subspace clustering

Romain Couillet; Abla Kammoun

doi:10.1109/ACSSC.2016.7869000

Random matrix improved subspace clustering

Romain Couillet, Abla Kammoun

Computer, Electrical and Mathematical Sciences and Engineering

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

4 Scopus citations

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.

Original language	English (US)
Title of host publication	2016 50th Asilomar Conference on Signals, Systems and Computers
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	90-94
Number of pages	5
ISBN (Print)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869000
State	Published - Mar 6 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Couillet’s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).

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10.1109/ACSSC.2016.7869000

Cite this

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title = "Random matrix improved subspace clustering",

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.",

author = "Romain Couillet and Abla Kammoun",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: Couillet{\textquoteright}s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).",

year = "2017",

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doi = "10.1109/ACSSC.2016.7869000",

language = "English (US)",

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AU - Couillet, Romain

AU - Kammoun, Abla

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: Couillet’s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).

PY - 2017/3/6

Y1 - 2017/3/6

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/623206

UR - http://ieeexplore.ieee.org/document/7869000/

UR - http://www.scopus.com/inward/record.url?scp=85016241517&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2016.7869000

DO - 10.1109/ACSSC.2016.7869000

M3 - Conference contribution

SN - 9781538639542

SP - 90

EP - 94

BT - 2016 50th Asilomar Conference on Signals, Systems and Computers

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Random matrix improved subspace clustering

Abstract

Bibliographical note

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