Model-Based Clustering Based on Variational Learning of Hierarchical Infinite Beta-Liouville Mixture Models

被引：0
|
作者：
Wentao Fan
Nizar Bouguila
机构：
[1] Huaqiao University,Department of Computer Science and Technology
[2] Concordia University,The Concordia Institute for Information Systems Engineering (CIISE)
来源：
Neural Processing Letters | 2016年 / 44卷
关键词：
Mixture models; Beta-Liouville; Variational Bayes ; Nonparametric Bayesian; Hierarchical Dirichlet process; Visual scenes categorization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this work, we develop a statistical framework for data clustering which uses hierarchical Dirichlet processes and Beta-Liouville distributions. The parameters of this framework are leaned using two variational Bayes approaches. The first one considers batch settings and the second one takes into account the dynamic nature of real data. Experimental results based on a challenging problem namely visual scenes categorization demonstrate the merits of the proposed framework.
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页码：431 / 449
页数：18
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