Frame-based Data Factorizations

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

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/worksArticle in conference proceedingsResearchpeer-review

Harvard

Mair, S, Boubekki, A & Brefeld, U 2017, Frame-based Data Factorizations. in D Precup & YW Teh (eds), 34th International Conference on Machine Learning, ICML 2017. Proceedings of Machine Learning Research, vol. 70, Curran Associates, Red Hook, pp. 2305-2313, International Conference on Machine Learning - ICML 2017, Sydney, New South Wales, Australia, 06.08.17. <http://proceedings.mlr.press/v70/mair17a.html>

APA

Mair, S., Boubekki, A., & Brefeld, U. (2017). Frame-based Data Factorizations. In D. Precup, & Y. W. Teh (Eds.), 34th International Conference on Machine Learning, ICML 2017 (pp. 2305-2313). (Proceedings of Machine Learning Research; Vol. 70). Curran Associates. http://proceedings.mlr.press/v70/mair17a.html

Vancouver

Mair S, Boubekki A, Brefeld U. Frame-based Data Factorizations. In Precup D, Teh YW, editors, 34th International Conference on Machine Learning, ICML 2017. Red Hook: Curran Associates. 2017. p. 2305-2313. (Proceedings of Machine Learning Research).

Bibtex

@inbook{ec0bf997797a4fe6b34dd8ecbb9128ce,
title = "Frame-based Data Factorizations",
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.",
keywords = "Business informatics",
author = "Sebastian Mair and Ahc{\`e}ne Boubekki and Ulf Brefeld",
note = "This work has been funded in parts by the German Federal Ministry of Education and Science BMBF under grant QQM/01LSA1503C; International Conference on Machine Learning - ICML 2017 : Thirty-fourth International Conference on Machine Learning, ICML 2017 ; Conference date: 06-08-2017 Through 11-08-2017",
year = "2017",
month = jul,
day = "25",
language = "English",
series = "Proceedings of Machine Learning Research",
publisher = "Curran Associates",
pages = "2305--2313",
editor = "Doina Precup and Teh, {Yee Whye}",
booktitle = "34th International Conference on Machine Learning, ICML 2017",
address = "United States",
url = "https://icml.cc/Conferences/2017",

}

RIS

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 -