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Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling

文献类型：期刊论文

作者Ma, Zhanyu1; Lai, Yuping2; Kleijn, W. Bastiaan3; Song, Yi-Zhe4; Wang, Liang5; Guo, Jun1
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2019-02-01
卷号30期号:2页码:449-463
关键词Bayesian estimation computer vision Dirichlet process (DP) mixture inverted Dirichlet distribution variational learning
ISSN号2162-237X
DOI10.1109/TNNLS.2018.2844399
通讯作者Ma, Zhanyu(mazhanyu@bupt.edu.cn)
英文摘要In this paper, we develop a novel variational Bayesian learning method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements. The recently proposed extended variational inference (EVI) framework is adopted to derive an analytically tractable solution. The convergency of the proposed algorithm is theoretically guaranteed by introducing single lower bound approximation to the original objective function in the EVI framework. In principle, the proposed model can be viewed as an infinite inverted Dirichlet mixture model that allows the automatic determination of the number of mixture components from data. Therefore, the problem of predetermining the optimal number of mixing components has been overcome. Moreover, the problems of overfitting and underfitting are avoided by the Bayesian estimation approach. Compared with several recently proposed DP-related methods and conventional applied methods, the good performance and effectiveness of the proposed method have been demonstrated with both synthesized data and real data evaluations.
WOS关键词PARALLEL FRAMEWORK ; TEXT DETECTION ; SELECTION ; CHANNELS ; VIDEO ; TIME
资助项目National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61773071] ; Beijing Nova Program[Z171100001117049] ; Beijing Nova Program Interdisciplinary Cooperation[Z181100006218137] ; Beijing Natural Science Foundation[4162044] ; Beijing Natural Science Foundation[KZ201810009011] ; Beijing Education Commission[KZ201810009011]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000457114600010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Nova Program ; Beijing Nova Program Interdisciplinary Cooperation ; Beijing Natural Science Foundation ; Beijing Education Commission
源URL[http://ir.ia.ac.cn/handle/173211/25325]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Ma, Zhanyu
作者单位1.Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligent Syst Lab, Beijing 100876, Peoples R China
2.North China Univ Technol, Dept Informat Secur, Beijing 100144, Peoples R China
3.Victoria Univ Wellington, Commun & Signal Proc Grp, Wellington 6140, New Zealand
4.Queen Mary Univ London, Sch Elect Engn & Comp Sci, SketchX Lab, London E1 4NS, England
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Zhanyu,Lai, Yuping,Kleijn, W. Bastiaan,et al. Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(2):449-463.
APA Ma, Zhanyu,Lai, Yuping,Kleijn, W. Bastiaan,Song, Yi-Zhe,Wang, Liang,&Guo, Jun.(2019).Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(2),449-463.
MLA Ma, Zhanyu,et al."Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.2(2019):449-463.

入库方式： OAI收割

来源：自动化研究所

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