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Reduction of Gaussian mixture models by maximum similarity
Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee tha...
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Published in: | Journal of nonparametric statistics 2010-08, Vol.22 (6), p.703-709 |
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container_title | Journal of nonparametric statistics |
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creator | Harmse, Jørgen E. |
description | Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee that they find the most similar. I give a method to find the most similar pair of components without comparing all pairs, and I propose an extension to higher dimensions. |
doi_str_mv | 10.1080/10485250903377293 |
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subjects | 60-04 62-07 Algorithms Comparative analysis Estimating techniques Gaussian Gaussian mixture models Iterative methods kernel density estimation machine learning and neural networks Mathematical models Merging model simplification Nonparametric statistics Normal distribution Reduction Similarity |
title | Reduction of Gaussian mixture models by maximum similarity |
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