Random matrix improved subspace clustering

Romain Couillet, Abla Kammoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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 languageEnglish (US)
Title of host publication2016 50th Asilomar Conference on Signals, Systems and Computers
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages90-94
Number of pages5
ISBN (Print)9781538639542
DOIs
StatePublished - Mar 6 2017

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