PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:1565
Deposited By:Jacob Goldberger
Deposited On:28 November 2005