Repairing Faulty Mixture Models using Density Estimation (2001)
Tags
Astrostatistics, Cached Sufficient Statistics, Clustering, Efficient Statistical Algorithms, Kd-trees and Ball-trees, Mixture Models, Spatial Statistics, Statistical Data Mining for Astrophysics
Abstract
Previous work in mixture model clustering has focused primarily on the issue of model selection. Model scoring functions (including penalized likelihood and Bayesian approxi- mations) can guide a search of the model pa- rameter and structure space. Relatively lit- tle research has addressed the issue of how to move through this space. Local optimization techniques, such as expectation maximization, solve only part of the problem; we still need to move between different local optima.
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Approximate BibTeX Entry
@inproceedings{sand-repairing,
Year = {2001},
Pages = {457-464},
Publisher = {Morgan Kaufmann, San Francisco, CA},
Booktitle = {International Conference on Machine Learning},
Author = {
Peter Sand, Andrew Moore
},
Title = {Repairing Faulty Mixture Models using Density Estimation}
}