...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge and Information Systems >A distributed EM algorithm to estimate the parameters of a finite mixture of components
【24h】

A distributed EM algorithm to estimate the parameters of a finite mixture of components

机译：分布式EM算法估计组分有限混合的参数

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a distributed expectation maximization (DEM) algorithm is first introduced in a general form for estimating the parameters of a finite mixture of components. This algorithm is used for density estimation and clustering of data distributed over nodes of a network. Then, a distributed incremental EM algorithm (DIEM) with a higher convergence rate is proposed. After a full derivation of distributed EM algorithms, convergence of these algorithms is analyzed based on the negative free energy concept used in statistical physics. An analytical approach is also developed for evaluating the convergence rate of both incremental and distributed incremental EM algorithms. It is analytically shown that the convergence rate of DIEM is much faster than that of the DEM algorithm. Finally, simulation results approve that DIEM remarkably outperforms DEM for both synthetic and real data sets.
机译：在本文中，首先以一般形式介绍了分布式期望最大化（DEM）算法，用于估计组分的有限混合的参数。该算法用于密度估计和对分布在网络节点上的数据进行聚类。然后，提出了一种具有较高收敛速度的分布式增量EM算法（DIEM）。完全推导了分布式EM算法之后，基于统计物理学中使用的负自由能概念分析了这些算法的收敛性。还开发了一种分析方法，用于评估增量式和分布式增量式EM算法的收敛速度。分析表明，DIEM的收敛速度比DEM算法的收敛速度快得多。最后，仿真结果证明DIEM对于综合数据集和实际数据集均明显优于DEM。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号