An Enhanced EM Algorithm Using Maximum Entropy Distribution as Initial Condition

被引：0
|
作者：
Xuan, Guorong [1 ]
Shi, Yun Q. [2 ]
Chai, Peiqi [1 ]
Sutthiwan, Patchara [2 ]
机构：
[1] Tongji Univ, Dept Comp Sci, Shanghai 200092, Peoples R China
[2] New Jersey Inst Technol, Dept ECE, Newark, NJ USA
来源：
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) | 2012年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The conventional EM algorithms may suffer from the following two problems. First, it may converge to a local maximum. Second, the algorithm may suffer from singularity. A novel Enhanced EM algorithm (EEM) using a realization of maximum-entropy uniform distribution as initial condition is proposed. A global optimal solution can be obtained. In addition, a positive perturbation scheme is adopted to avoid singularity. Experimental results have demonstrated that the EEM is simple and effective compared with some prior arts.
引用
收藏
页码：849 / 852
页数：4
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