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Bayesian Estimation of Generalized Gamma Mixture Model Based on Variational EM Algorithm

Liu, Chi and Li, Heng-Chao and Fu, Kun and Datcu, Mihai and Emery, William (2019) Bayesian Estimation of Generalized Gamma Mixture Model Based on Variational EM Algorithm. Pattern Recognition, 87, pp. 269-284. Elsevier. DOI: 10.1016/j.patcog.2018.10.025 ISSN 0031-3203

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Official URL: https://www.sciencedirect.com/science/article/abs/pii/S0031320318303789


In this paper, we propose a Bayesian inference method for the generalized Gamma mixture model (GΓMM) based on variational expectation-maximization algorithm. Specifically, the shape parameters, the inverse scale parameters, and the mixing coefficients in the GΓMM are treated as random variables, while the power parameters are left as parameters without assigning prior distributions. The help function is designed to approximate the lower bound of the variational objective function, which facilitates the assignment of the conjugate prior distributions and leads to the closed-form update equations. On this basis, the variational E-step and the variational M-step are alternatively implemented to infer the posteriors of the variables and estimate the parameters. The computational demand is reduced by the proposed method. More importantly, the effective number of components of the GΓMM can be determined automatically. The experimental results demonstrate the effectiveness of the proposed method especially in modeling the asymmetric and heavy-tailed data.

Item URL in elib:https://elib.dlr.de/123456/
Document Type:Article
Title:Bayesian Estimation of Generalized Gamma Mixture Model Based on Variational EM Algorithm
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Liu, ChiSouthwest Jiaotong UniversityUNSPECIFIED
Li, Heng-ChaoChinese Academy of SciencesUNSPECIFIED
Fu, KunBeijing University of Chemical TechnologyUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Emery, WilliamUniversity of Colorado at BoulderUNSPECIFIED
Journal or Publication Title:Pattern Recognition
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.patcog.2018.10.025
Page Range:pp. 269-284
Keywords:Finite mixture models, Generalized Gamma distribution, Variational expectation-maximization (VEM), Maximum likelihood estimation, Extended factorized approximation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:20 Dec 2018 11:30
Last Modified:20 Dec 2018 11:30

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