Hierarchical clustering of a mixture model
Jacob Goldberger and Sam Roweis
In: NIPS 2004, 13-18 Dec 2004, Vancover, Canada.
In this paper we propose an efficient algorithm for reducing a large
mixture of Gaussians into a smaller mixture while still preserving the
component structure of the original model; this is achieved by
clustering (grouping) the components.
The method minimizes a new, easily computed distance measure between
two Gaussian mixtures that can be motivated from a suitable stochastic
model and the iterations of the algorithm use only the model
parameters, avoiding the need for explicit resampling of datapoints.
We demonstrate the method by performing hierarchical clustering of
scenery images and handwritten digits.