CASIA OpenIR
Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling
Ma, Zhanyu1; Lai, Yuping2; Kleijn, W. Bastiaan3; Song, Yi-Zhe4; Wang, Liang5; Guo, Jun1
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2019-02-01
Volume30Issue:2Pages:449-463
Corresponding AuthorMa, Zhanyu(mazhanyu@bupt.edu.cn)
AbstractIn 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.
KeywordBayesian estimation computer vision Dirichlet process (DP) mixture inverted Dirichlet distribution variational learning
DOI10.1109/TNNLS.2018.2844399
WOS KeywordPARALLEL FRAMEWORK ; TEXT DETECTION ; SELECTION ; CHANNELS ; VIDEO ; TIME
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000457114600010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:39[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25326
Collection中国科学院自动化研究所
Corresponding AuthorMa, Zhanyu
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ma, Zhanyu]'s Articles
[Lai, Yuping]'s Articles
[Kleijn, W. Bastiaan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ma, Zhanyu]'s Articles
[Lai, Yuping]'s Articles
[Kleijn, W. Bastiaan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ma, Zhanyu]'s Articles
[Lai, Yuping]'s Articles
[Kleijn, W. Bastiaan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.