Matrix factorization as search

Authors:
Kristian Kersting;Christian Bauckhage;Christian Thurau;Mirwaes Wahabzada
Affiliations:
Fraunhofer IAIS, Sankt Augustin, Germany, Institute of Geodesy and Geoinformation, University of Bonn, Germany;Fraunhofer IAIS, Sankt Augustin, Germany;Game Analytics Aps., Copenhagen, Denmark;Fraunhofer IAIS, Sankt Augustin, Germany
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Year:
2012

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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.