@inproceedings{5134,
  title = {A Decoupled Approach to Exemplar-based Unsupervised Learning},
  journal = {Proceedings of the 25th International Conference on Machine Learning (ICML 2008)},
  booktitle = {ICML 2008},
  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.},
  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},
  slug = {5134},
  author = {Nowozin, S. and BakIr, G.},
  month_numeric = {7}
}