Hierarchical Clustering of a Mixture Model

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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Jacob Goldberger, Sam T. Roweis
• Keywords: Computed Distance Measure, NIPS 2004, NIPS 2007, Smaller Mixture, Suitable Stochastic Model