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

页码：849 / 852

页数：4

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