Paper Info
Title
Extended variational inference for Dirichlet process mixture of Beta-Liouville distributions for proportional data modeling
Abstract
Bayesian estimation of parameters in the Dirichlet mixture process of the Beta-Liouville distribution (i.e., the infinite Beta-Liouville mixture model) has recently gained considerable attention due to its modeling capability for proportional data. However, applying the conventional variational inference (VI) framework cannot derive an analytically tractable solution since the variational objective function cannot be explicitly calculated. In this paper, we adopt the recently proposed extended VI framework to derive the closed-form solution by further lower bounding the original variational objective function in the VI framework. This method is capable of simultaneously determining the model's complexity and estimating the model's parameters. Moreover, due to the nature of Bayesian nonparametric approaches, it can also avoid the problems of underfitting and overfitting. Extensive experiments were conducted on both synthetic and real data, generated from two real-world challenging applications, namely, object detection and text categorization, and its superior performance and effectiveness of the proposed method have been demonstrated.
Year
DOI
Venue
2022
10.1002/int.22721
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Keywords
DocType
Volume
bayesian estimation, beta-Liouville distribution, dirichlet process, extended variational inference, infinite mixture model, object detection, text categorization
Journal
37
Issue
ISSN
Citations 
7
0884-8173
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Yuping Lai100.68
Wenbo Guan200.34
Lijuan Luo300.34
Qiang Ruan400.68
Yuan Ping500.34
Heping Song611.02
Hongying Meng783269.39
Yu Pan800.34