Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference

doi:10.1109/TNNLS.2021.3072209

	Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference
	Zhanyu Ma 1; Yuping Lai 2; Jiyang Xie 1; Deyu Meng 3,4; W. Bastiaan Kleijn 5; Jun Guo 1; Jingyi Yu6
	2022-11-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (IF:10.2[JCR-2023],10.4[5-Year])
ISSN	2162-2388
卷号	33 期号:11
发表状态	已发表
DOI	10.1109/TNNLS.2021.3072209
摘要	A Bayesian nonparametric approach for estimation of a Dirichlet process (DP) mixture of generalized inverted Dirichlet distributions [i.e., an infinite generalized inverted Dirichlet mixture model (InGIDMM)] has been proposed. The generalized inverted Dirichlet distribution has been proven to be efficient in modeling the vectors that contain only positive elements. Under the classical variational inference (VI) framework, the key challenge in the Bayesian estimation of InGIDMM is that the expectation of the joint distribution of data and variables cannot be explicitly calculated. Therefore, numerical methods are usually applied to simulate the optimal posterior distributions. With the recently proposed extended VI (EVI) framework, we introduce lower bound approximations to the original variational objective function in the VI framework such that an analytically tractable solution can be derived. Hence, the problem in numerical simulation has been overcome. By applying the DP mixture technique, an InGIDMM can automatically determine the number of mixture components from the observed data. Moreover, the DP mixture model with an infinite number of mixture components also avoids the problems of underfitting and overfitting. The performance of the proposed approach is demonstrated with both synthesized data and real-life data applications.
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收录类别	SCI ; SCIE ; EI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/135745
专题	信息科学与技术学院信息科学与技术学院_PI研究组_虞晶怡组
作者单位	1.Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China 2.School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China 3.School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China 4.Faculty of Information Technology, Macau University of Science and Technology, Macau, China 5.School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand 6.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Zhanyu Ma,Yuping Lai,Jiyang Xie,et al. Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(11).
APA	Zhanyu Ma.,Yuping Lai.,Jiyang Xie.,Deyu Meng.,W. Bastiaan Kleijn.,...&Jingyi Yu.(2022).Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(11).
MLA	Zhanyu Ma,et al."Dirichlet Process Mixture of Generalized Inverted Dirichlet Distributions for Positive Vector Data With Extended Variational Inference".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.11(2022).