Journal of South China University of Technology (Natural Science Edition) ›› 2009, Vol. 37 ›› Issue (10): 101-107.

• Computer Science & Technology • Previous Articles     Next Articles

Automatic Estimation Algorithm Mixture Model Based on of Component Number of Penalized Distance

Zhang Da-ming1 Fu Mao-sheng1 Guo Hui2  Luo Bin1   

  1. 1.School of Computer Science and Technology, Anhui University, Hefei 230039, Anhui, China; 2. The University of New South Wales, Sydney NSW2052, Australia
  • Received:2008-11-20 Revised:2009-03-25 Online:2009-10-25 Published:2009-10-25
  • Contact: 张大明（1976-），男，安徽建筑工业学院讲师，在职博士生，主要从事模式识别和图像处理研究． E-mail:zhang_daruing@yahoo．com．cn
  • About author:张大明（1976-），男，安徽建筑工业学院讲师，在职博士生，主要从事模式识别和图像处理研究．
  • Supported by:

    国家自然科学基金资助项目（60772122）;高等学校博士学科点专项科研基金资助项目（20070357001）;安徽省高等学校自然科学研究重点项目（KJ2007A045）

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Abstract:

The expectation-maximization （EM） algorithm is a popular approach to the parameter estimation of the finite mixture model （FMM）. A drawback of this approach is that the number of components of the FMM is not known in advance. In this paper, a penalized minimum matching distance-guided EM algorithm is discussed. Then, under the framework of Greedy EM, an automatic algorithm with high speed and accuracy is proposed to esti- mate the component number of the Gaussian mixture model. The effectiveness of the proposed algorithm is finally verified by the simulations of univariate and bivariate Gaussian mixture models.

Key words: finite mixture model, component number, penalized minimum matching distance, Greedy EM, Parzen window, bandwidth