Frame-based Data Factorizations
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Frame-based Data Factorizations. / Mair, Sebastian; Boubekki, Ahcène; Brefeld, Ulf.
34th International Conference on Machine Learning, ICML 2017. ed. / Doina Precup; Yee Whye Teh. Red Hook : Curran Associates, 2017. p. 2305-2313 (Proceedings of Machine Learning Research; Vol. 70).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Frame-based Data Factorizations
AU - Mair, Sebastian
AU - Boubekki, Ahcène
AU - Brefeld, Ulf
N1 - Conference code: 34
PY - 2017/7/25
Y1 - 2017/7/25
N2 - 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.
AB - 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.
KW - Business informatics
UR - http://proceedings.mlr.press/v70/mair17a.html
UR - http://www.scopus.com/inward/record.url?scp=85048477893&partnerID=8YFLogxK
M3 - Article in conference proceedings
T3 - Proceedings of Machine Learning Research
SP - 2305
EP - 2313
BT - 34th International Conference on Machine Learning, ICML 2017
A2 - Precup, Doina
A2 - Teh, Yee Whye
PB - Curran Associates
CY - Red Hook
T2 - International Conference on Machine Learning - ICML 2017
Y2 - 6 August 2017 through 11 August 2017
ER -