Collapsed Variational Dirichlet Process Mixture Models

机译：折叠变分Dirichlet过程混合模型

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Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today's datasets, computational efficiency becomes an essential ingredient in the applicability of these techniques to real world data. We study and experimentally compare a number of variational Bayesian (VB) approximations to the DP mixture model. In particular we consider the standard VB approximation where parameters are assumed to be independent from cluster assignment variables, and a novel collapsed VB approximation where mixture weights are marginalized out. For both VB approximations we consider two different ways to approximate the DP, by truncating the stick-breaking construction, and by using a finite mixture model with a symmetric Dirichlet prior.

机译：非参数贝叶斯混合模型，特别是Dirichlet过程（DP）混合模型，显示出对密度估计和数据聚类的巨大希望。考虑到当今数据集的规模，计算效率已成为这些技术在现实世界数据中的适用性的重要组成部分。我们研究和实验比较了DP混合模型的许多变分贝叶斯（VB）近似值。特别是，我们考虑了标准的VB近似，其中参数被认为与群集分配变量无关，并且考虑了一种新颖的折叠VB近似，其中混合权重被边缘化了。对于这两种VB近似，我们考虑了两种不同的近似DP的方法，即截断不连续的构造，并使用具有对称Dirichlet先验的有限混合模型。

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《Twentieth International Joint Conference on Artificial Intelligence(IJCAI-07)》|2007年|P.27962801|共2页
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Kenichi Kurihara; Max Welling; Yee Whye Teh;
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中图分类人工智能理论;
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Collapsed Variational Dirichlet Process Mixture Models

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