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

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• 作者：Wentao Fan ; Nizar Bouguila
• 关键词：Mixture models ; Beta ; Liouville ; Variational Bayes ; Nonparametric Bayesian ; Hierarchical Dirichlet process ; Visual scenes categorization
• 刊名：Neural Processing Letters
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：44
• 期：2
• 页码：431-449
• 全文大小：964 KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Complexity
  Artificial Intelligence and Robotics
  Electronic and Computer Engineering
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-773X
• 卷排序：44

文摘

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.