TY - GEN
T1 - Random matrix improved subspace clustering
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 -