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id: 37658

Frame-based data factorizations Mair, Sebastian; Boubekki, Ahcène; Brefeld, Ulf Book Chapter | Aus: 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 37658 Endnote: Author(s): Mair, Sebastian; Boubekki, Ahcène; Brefeld, Ulf
Title: Frame-based data factorizations
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 (Proceedings of Machine Learning Research, 70), S. 2305-2313
URL: http://proceedings.mlr.press/v70/mair17a/mair17a.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Algorithmus; Automatisierung; Computerlinguistik; Daten; Datenanalyse; Methode; Verfahren
Abstract: 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.)
DIPF-Departments: Informationszentrum Bildung