Abstract
Simplex Volume Maximization (SiVM) exploits distance geometry for efficiently factorizing gigantic matrices. It was proven successful in game, social media, and plant mining. Here, we review the distance geometry approach and argue that it generally suggests to factorize gigantic matrices using search-based instead of optimization techniques.
The authors would like to thank the anonymous reviewers for their comments. This work was partly supported by the Fraunhofer ATTRACT fellowship STREAM.
Chapter PDF
Similar content being viewed by others
Keywords
- Singular Value Decomposition
- Matrix Factorization
- Distance Geometry
- Internet Image
- Fourier Basis Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Blumenthal, L.M.: Theory and Applications of Distance Geometry. Oxford University Press (1953)
Civril, A., Magdon-Ismail, M.: On Selecting A Maximum Volume Sub-matrix of a Matrix and Related Problems. TCS 410(47-49), 4801–4811 (2009)
Cutler, A., Breiman, L.: Archetypal Analysis. Technometr. 36(4), 338–347 (1994)
Kersting, K., Wahabzada, M., Roemer, C., Thurau, C., Ballvora, A., Rascher, U., Leon, J., Bauckhage, C., Pluemer, L.: Simplex distributions for embedding data matrices over time. In: SDM (2012)
Kersting, K., Xu, Z., Wahabzada, M., Bauckhage, C., Thurau, C., Roemer, C., Ballvora, A., Rascher, U., Leon, J., Pluemer, L.: Pre–symptomatic prediction of plant drought stress using dirichlet–aggregation regression on hyperspectral images. In: AAAI — Computational Sustainability and AI Track (2012)
Mahoney, M.W., Drineas, P.: CUR Matrix Decompositions for Improved Data Analysis. PNAS 106(3), 697–702 (2009)
Thurau, C., Kersting, K., Bauckhage, C.: Yes We Can – Simplex Volume Maximization for Descriptive Web-Scale Matrix Factorization. In: Proc. CIKM (2010)
Thurau, C., Kersting, K., Bauckhage, C.: Deterministic CUR for improved large–scale data analysis: An empirical study. In: SDM (2012)
Thurau, C., Kersting, K., Wahabzada, M., Bauckhage, C.: Descriptive matrix factorization for sustainability: Adopting the principle of opposites. DAMI 24(2), 325–354 (2012)
Torralba, A., Fergus, R., Freeman, W.T.: 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(11), 1958–1970 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kersting, K., Bauckhage, C., Thurau, C., Wahabzada, M. (2012). Matrix Factorization as Search. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-33486-3_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33485-6
Online ISBN: 978-3-642-33486-3
eBook Packages: Computer ScienceComputer Science (R0)