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