Proceedings of the 1998 conference on Advances in neural information processing systems II
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
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The mixture modeling framework is widely used in many applications. In this paper, we propose a component reductiontechnique, that collapses a mixture model into a mixture with fewer components. For fitting a mixture model to data, the EM (Expectation-Maximization) algorithm is usually used. Our algorithm is derived by extending mixture model learning using the EM-algorithm.In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem.