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	Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning
	Ma, Jinwen ; Liu, Jianfeng ; Ren, Zhijie
	2009
关键词	Bayesian Ying-Yang (BYY) harmony learning Poisson mixture Gradient learning algorithm Automated model selection Texture classification GAUSSIAN MIXTURE EM ALGORITHM TEXTURE CLASSIFICATION FINITE MIXTURE DISTRIBUTIONS NUMBER COMPONENTS
英文摘要	Finite mixture is widely used in the fields of information processing and data analysis. However, its model selection, i.e., the selection of components in the Mixture for a given sample data set, has been still a rather difficult task. Recently, the Bayesian Ying-Yang (BYY) harmony learning has provided a new approach to the Gaussian mixture modeling with a favorite feature that model selection can be made automatically during parameter learning. In this paper, based on the same BYY harmony learning framework for finite mixture, we propose an adaptive gradient BYY learning algorithm for Poisson mixture with automated model selection. It is demonstrated well by the simulation experiments that this adaptive gradient BYY learning algorithm can automatically determine the number of actual Poisson components for a sample data set, with a good estimation of the parameters in the Original or true mixture where the components are separated in a certain degree. Moreover, the adaptive gradient BYY learning algorithm is successfully applied to texture classification. (C) 2009 Elsevier Ltd. All rights reserved.; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; SCI(E); 3; ARTICLE; 11; 2659-2670; 42
语种	英语
出处	SCI
出版者	模式识别
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/157697]
专题	数学科学学院
推荐引用方式 GB/T 7714	Ma, Jinwen,Liu, Jianfeng,Ren, Zhijie. Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning. 2009-01-01.

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