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	Dynamically regularized harmony learning of Gaussian mixtures
	Wang, Hongyan ; Ma, Jinwen
	2014
英文摘要	In this paper, a dynamically regularized harmony learning (DRHL) algorithm is proposed for Gaussian mixture learning with a favourite feature of both adaptive model selection and consistent parameter estimation. Specifically, under the framework of Bayesian Ying-Yang (BYY) harmony learning, we utilize the average Shannon entropy of the posterior probability per sample as a regularization term being controlled by a scale factor to the harmony function on Gaussian mixtures increasing from 0 to 1 dynamically. It is demonstrated by the experiments on both synthetic and realworld datasets that the DRHL algorithm can not only select the correct number of actual Gaussians in the dataset, but also obtain the maximum likelihood (ML) estimators of the parameters in the actual mixture. Moreover, the DRHL algorithm is scalable and can be implemented on a big dataset. ? 2014 IEEE.; EI; January; 1158-1164; 2014-January
语种	英语
出处	2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
DOI标识	10.1109/SMC.2014.6974070
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423956]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wang, Hongyan,Ma, Jinwen. Dynamically regularized harmony learning of Gaussian mixtures. 2014-01-01.

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