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A Decoupled Approach to Exemplar-based Unsupervised Learning

2008

Conference Paper

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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.

Author(s): Nowozin, S. and BakIr, G.
Book Title: ICML 2008
Journal: Proceedings of the 25th International Conference on Machine Learning (ICML 2008)
Pages: 704-711
Year: 2008
Month: July
Day: 0
Editors: Cohen, W. W., A. McCallum, S. Roweis
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: New York, NY, USA
Digital: 0
DOI: 10.1145/1390156.1390245
Event Name: 25th International Conference on Machine Learning
Event Place: Helsinki, Finland
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5134,
  title = {A Decoupled Approach to Exemplar-based Unsupervised Learning},
  author = {Nowozin, S. and BakIr, G.},
  journal = {Proceedings of the 25th International Conference on Machine Learning (ICML 2008)},
  booktitle = {ICML 2008},
  pages = {704-711},
  editors = {Cohen, W. W., A. McCallum, S. Roweis},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jul,
  year = {2008},
  month_numeric = {7}
}