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AbstractThis 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.
CitationCouillet 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.
SponsorsCouillet’s work is supported by the ANR Project RMT4GRAPH (ANR-14-CE28-0006).
Conference/Event name50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016