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On Fast Approximate Submodular Minimization

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83994

Jegelka,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83814

Bilmes,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Jegelka, S., Lin, H., & Bilmes, J. (2012). On Fast Approximate Submodular Minimization. In Advances in Neural Information Processing Systems 24 (pp. 460-468). Red Hook, NY, USA: Curran.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B880-6
Zusammenfassung
We are motivated by an application to extract a representative subset of machine learning training data and by the poor empirical performance we observe of the popular minimum norm algorithm. In fact, for our application, minimum norm can have a running time of about O(n7) (O(n5) oracle calls). We therefore propose a fast approximate method to minimize arbitrary submodular functions. For a large sub-class of submodular functions, the algorithm is exact. Other submodular functions are iteratively approximated by tight submodular upper bounds, and then repeatedly optimized. We show theoretical properties, and empirical results suggest significant speedups over minimum norm while retaining higher accuracies.