Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization
Date
2010-08
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Source Title
Proceedings - 20th International Conference on Pattern Recognition, 2010
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Publisher
IEEE
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Pages
746 - 749
Language
English
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2
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25
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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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Keywords
Candidate solution, Clustering problems, Covariance matrices, Gaussian mixture model, Matrix, Optimization formulations, Population-based algorithm, Blind source separation, Clustering algorithms, Covariance matrix, Optical communication, Particle swarm optimization (PSO), Pattern recognition, Maximum likelihood estimation