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K-Means clustering via the Frank-Wolfe algorithm

 
: Bauckhage, C.

:
Volltext ()

Krestel, R.:
LWDA 2016, Lernen, Wissen, Daten, Analysen : Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" Potsdam, Germany, September 12-14, 2016
Potsdam, 2016 (CEUR Workshop Proceedings 1670)
http://ceur-ws.org/Vol-1670/
ISSN: 1613-0073
S.311-322
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2016, Potsdam>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
We show that k-means clustering is a matrix factorization problem. Seen from this point of view, k-means clustering can be computed using alternating least squares techniques and we show how the constrained optimization steps involved in this procedure can be solved efficiently using the Frank-Wolfe algorithm.

: http://publica.fraunhofer.de/dokumente/N-422587.html