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Communication Dans Un Congrès Année : 2016

Random Matrix Improved Subspace Clustering

Résumé

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.
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Dates et versions

hal-01633444 , version 1 (19-05-2020)

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Romain Couillet, Abla Kammoun. Random Matrix Improved Subspace Clustering. 50th Asilomar Conference on Signals, Systems and Computers, Nov 2016, Pacific Grove, United States. ⟨10.1109/acssc.2016.7869000⟩. ⟨hal-01633444⟩
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