Frame-based Data Factorizations

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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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.
OriginalspracheEnglisch
Titel34th International Conference on Machine Learning, ICML 2017
HerausgeberDoina Precup, Yee Whye Teh
Anzahl der Seiten9
ErscheinungsortRed Hook
VerlagCurran
Erscheinungsdatum25.07.2017
Seiten2305-2313
ISBN (elektronisch)978-1-5108-5514-4
PublikationsstatusErschienen - 25.07.2017
VeranstaltungInternational Conference on Machine Learning - ICML 2017: Thirty-fourth International Conference on Machine Learning - International Convention Centre, Sydney , Sydney, Australien
Dauer: 06.08.201711.08.2017
Konferenznummer: 34
https://icml.cc/Conferences/2017

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