PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Decoupled Approach to Exemplar-based Unsupervised Learning
Sebastian Nowozin and Gökhan BakIr
In: ICML 2008, 05-09 Jul 2008, Helsinki, Finland.

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 well-behaved and can be solved efficiently.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4736
Deposited By:Sebastian Nowozin
Deposited On:24 March 2009