QQ客服 官方微博 反馈留言

	Asymptotic convergence properties of entropy regularized likelihood learning on finite mixtures with automatic model selection
	Lu, Zhiwu ; Lu, Xiaoqing ; Ye, Zhiyuan
	2007
英文摘要	In finite mixture modelling, it is crucial to select the number of components for a data set. We have proposed an entropy regularized likelihood (ERL) learning principle for the finite mixtures to solve this model selection problem under regularization theory. In this paper, we further give an asymptotic analysis of the ERL learning, and find that the global minimization of the ERL function in a simulated annealing way (i.e., the regularization factor is gradually reduced to zero) leads to automatic model selection on the finite mixtures with a good parameter estimation. As compared with the EM algorithm, the ERL learning can go across the local minima of the negative likelihood and keep robust with respect to initialization. The simulation experiments then prove our theoretic analysis. ? Springer-Verlag Berlin Heidelberg 2007.; EI; PART 1; 986-993; 4491 LNCS
语种	英语
出处	4th International Symposium on Neural Networks, ISNN 2007
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/461632]
专题	计算机科学技术研究所
推荐引用方式 GB/T 7714	Lu, Zhiwu,Lu, Xiaoqing,Ye, Zhiyuan. Asymptotic convergence properties of entropy regularized likelihood learning on finite mixtures with automatic model selection. 2007-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。