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Conference Paper

A Decoupled Approach to Exemplar-based Unsupervised Learning

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84113

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83791

BakIr,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Nowozin, S., & BakIr, G. (2008). A Decoupled Approach to Exemplar-based Unsupervised Learning. Proceedings of the 25th International Conference on Machine Learning (ICML 2008), 704-711.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C82F-C
Abstract
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possible prototypes to training exemplars. In particular, this has been done for clustering, vector quantization and mixture model density estimation. In this paper we propose a novel algorithm that is theoretically and practically superior to these convex formulations. This is possible by posing the unsupervised learning problem as a single convex master problem" with non-convex subproblems. We show that for the above learning tasks the subproblems are extremely wellbehaved and can be solved efficiently.