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Pattern recognition using boundary data of component distributions
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DOI：10.1016/j.cie.2010.08.007.png)
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摘要
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In statistical pattern recognition, a Gaussian mixture model is sometimes used for representing the distribution of vectors. The parameters of the Gaussian mixture model are usually estimated from given sample data by the expectation maximization algorithm. However, when the number of data attributes is large, the parameters cannot be estimated correctly. In this paper, we propose a novel approach for estimating the parameters of the Gaussian mixture model by using sample data located on the boundary of regions defined by the component density functions. Experiments are carried out to show the characteristics of the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Pattern recognition
Gaussian mixture model
Parameter estimation
High-dimensional vector
Probabilistic model
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6.5
论文数:
1.0W
被引数:
3.8W
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