TY - GEN
T1 - Hierarchical clustering of a mixture model
AU - Goldberger, Jacob
AU - Roweis, Sam
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84898983549&partnerID=8YFLogxK
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AN - SCOPUS:84898983549
SN - 0262195348
SN - 9780262195348
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PB - Neural information processing systems foundation
T2 - 18th Annual Conference on Neural Information Processing Systems, NIPS 2004
Y2 - 13 December 2004 through 16 December 2004
ER -