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Autor:
Mair, Sebastian; Boubekki, Ahcène; Brefeld, Ulf:

Titel:
Frame-based data factorizations

Quelle:
In: Precup, Doina; Teh, Yee Whye (Hrsg.): Proceedings of the International Conference on Machine Learning (IMCL 2017), 6-11 August 2017, International Convention Centre, Sydney, Australia Red Hook, NY : Curran (2017) , 2305-2313

Serie:
Proceedings of Machine Learning Research, 70

URL des Volltextes:
http://proceedings.mlr.press/v70/mair17a/mair17a.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Algorithmus, Automatisierung, Computerlinguistik, Daten, Datenanalyse, Methode, Verfahren


Abstract(original):
Archetypal Analysis is the method of choice to compute interpretable matrix factorizations. Every data point is represented as a convex combination of factors, i.e., points on the boundary of the convex hull of the data. This renders computation inefficient. In this paper, we show that the set of vertices of a convex hull, the so-called frame, can be efficiently computed by a quadratic program. We provide theoretical and empirical results for our proposed approach and make use of the frame to accelerate Archetypal Analysis. The novel method yields similar reconstruction errors as baseline competitors but is much faster to compute. (DIPF/Orig.)


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last modified Nov 11, 2016