Article ID Journal Published Year Pages File Type
1135779 Computers & Industrial Engineering 2011 7 Pages PDF
Abstract

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.

Research highlights► Novel approach for estimating Gaussian mixture models is proposed. ► Data on the boundary of regions defined by the component density functions are used. ► The proposed method is effective especially when the number of samples is small.

Related Topics
Physical Sciences and Engineering Engineering Industrial and Manufacturing Engineering
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