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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.
