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)|
|Number of pages||5|
|State||Published - Mar 6 2017|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Couillet’s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).