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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling
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Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling

机译：非高斯图像特征建模中逆狄利克雷分布的狄利克雷过程混合的变分贝叶斯学习

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摘要

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements. The recently proposed extended variational inference (EVI) framework is adopted to derive an analytically tractable solution. The convergency of the proposed algorithm is theoretically guaranteed by introducing single lower bound approximation to the original objective function in the EVI framework. In principle, the proposed model can be viewed as an infinite inverted Dirichlet mixture model that allows the automatic determination of the number of mixture components from data. Therefore, the problem of predetermining the optimal number of mixing components has been overcome. Moreover, the problems of overfitting and underfitting are avoided by the Bayesian estimation approach. Compared with several recently proposed DP-related methods and conventional applied methods, the good performance and effectiveness of the proposed method have been demonstrated with both synthesized data and real data evaluations.
机译：在本文中，我们为反向Dirichlet分布的Dirichlet过程（DP）混合物开发了一种新颖的变分贝叶斯学习方法，该方法已显示出对具有正元素的矢量建模非常灵活。采用了最近提出的扩展的变分推理（EVI）框架，以得出易于分析的解决方案。通过在EVI框架中向原始目标函数引入单个下界逼近，理论上可以保证所提出算法的收敛性。原则上，建议的模型可以看作是无限倒Dirichlet混合模型，该模型允许根据数据自动确定混合组分的数量。因此，克服了确定最佳混合组分数量的问题。此外，贝叶斯估计方法避免了过度拟合和拟合不足的问题。与最近提出的一些与DP相关的方法和常规应用的方法相比，已通过综合数据和实际数据评估证明了该方法的良好性能和有效性。

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