A Greedy Merge Learning Algorithm for Gaussian Mixture Model

被引：2
|
作者：
Li, Yan [1 ]
Li, Lei [2 ]
机构：
[1] Cent S Univ, Dept Probabil & Stat, Sch Math Sci & Comp Technol, Changsha 410075, Hunan, Peoples R China
[2] Peking Univ, Math Sci & LAMA, Dept Informat Sci, Beijing 100871, Peoples R China
来源：
2009 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL 2, PROCEEDINGS | 2009年
关键词：
Gaussian mixture model; EM algorithm; Model selection; Merge operation; Parameters estimation; EM ALGORITHM;
D O I：
10.1109/IITA.2009.273
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Gaussian mixture model (GMM) has been widely used in fields of image processing and investment data mining. However, in many practical applications, the number of the components is not known. This paper proposes a kind of greedy merge EM (GMEM) learning algorithm such that the number of Gaussians can be determined automatically with the minimum message length (MML) criterion. Moreover, the greedy merge learning algorithm is successfully applied to unsupervised data analysis. It is demonstrated well by the experiments that the proposed greedy merge EM (GMEM) learning algorithm can make both parameter learning and decide the number of the Gaussian mixture.
引用
收藏
页码：506 / +
页数：3
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