Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization

Date

2010-08

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Proceedings - 20th International Conference on Pattern Recognition, 2010

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IEEE

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Pages

746 - 749

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English

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Abstract

We present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows independent updating of individual parameters while retaining the validity of the matrix. The second solution involves an optimization formulation for finding correspondences between different parameter orderings of candidate solutions. The effectiveness of the proposed solutions are demonstrated on a novel clustering algorithm based on particle swarm optimization for the estimation of Gaussian mixture models. © 2010 IEEE.

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Published Version (Please cite this version)