Pattern recognition using boundary data of component distributions

Masako Omachi, Shinichiro Omachi, Hirotomo Aso, Tsuneo Saito

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)466-472
Number of pages7
JournalComputers and Industrial Engineering
Issue number3
Publication statusPublished - 2011 Apr


  • Gaussian mixture model
  • High-dimensional vector
  • Parameter estimation
  • Pattern recognition
  • Probabilistic model


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