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.
Sebastian Mair (Leuphana University Lüneburg)
Ahcène Boubekki (Leuphana University)
Ulf Brefeld (Leuphana University)
Related Events (a corresponding poster, oral, or spotlight)
2017 Talk: Frame-based Data Factorizations »
Mon Aug 7th 04:42 -- 05:00 AM Room C4.4