Cov-HGMEM: An Improved Hierarchical Clustering Algorithm

摘要：

In this paper we present an improved method for hierarchical clustering of Gaussian mixture components derived from Hierarchical Gaussian Mixture Expectation Maximization (HGMEM) algorithm.As HGMEM performs,it is efficient in reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original mode.Compared with HGMEM algorithm,it takes covariance into account in Expectation-Step without affecting the Maximization-Step,avoiding excessive expansion of some components,and we simply call it Cov-HGMEM.Image retrieval experiments indicate that our proposed algorithm outperforms previously suggested method.

作者: Sanming Song Qunsheng Yang Yinwei Zhan

作者单位: Faculty of Computer,Guangdong University of Technology 510006 Guangzhou,P.R.China

会议类型: 国际会议

会议名称: 4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 424-429

在线出版日期: 2008-01-16（万方平台首次上网日期，不代表论文的发表时间）