Hierarchical Clustering of a Mixture Model
Abstract
In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserv- ing 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 demon- strate the method by performing hierarchical clustering of scenery images and handwritten digits.
Cite
Text
Goldberger and Roweis. "Hierarchical Clustering of a Mixture Model." Neural Information Processing Systems, 2004.Markdown
[Goldberger and Roweis. "Hierarchical Clustering of a Mixture Model." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/goldberger2004neurips-hierarchical/)BibTeX
@inproceedings{goldberger2004neurips-hierarchical,
title = {{Hierarchical Clustering of a Mixture Model}},
author = {Goldberger, Jacob and Roweis, Sam T.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {505-512},
url = {https://mlanthology.org/neurips/2004/goldberger2004neurips-hierarchical/}
}