基于变分推理和MapReduce的主题模型扩展
Using Variational Inference and MapReduce to Scale Topic Modeling
摘要：
潜在狄利克雷分配（LDA）是一种用于探索文档集合的流行主题建模技术。由于大型数据集的日益普及，需要提高LDA推理的可扩展性。在本文中，我们提出了一种技术称为~ \emph{MapReduce LDA}（LDA先生），以适应非常大的语料库集合的MapReduce框架。与其他使用Gibbs抽样的LDA尺度推理技术相比，我们使用变分推理。我们的解决方案有效地分配计算，并且实现起来相对简单。更重要的是，与高度调优和专门化的实现不同，这种变式实现易于扩展。我们展示了两个扩展的模型可能与这个可扩展的框架：知情的先验知识，以指导主题发现和建模主题的多语言语料库。
英文摘要：
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference of LDA. In this paper, we propose a technique called ~\emph{MapReduce LDA} (Mr. LDA) to accommodate very large corpus collections in the MapReduce framework. In contrast to other techniques to scale inference for LDA, which use Gibbs sampling, we use variational inference. Our solution efficiently distributes computation and is relatively simple to implement. More importantly, this variational implementation, unlike highly tuned and specialized implementations, is easily extensible. We demonstrate two extensions of the model possible with this scalable framework: informed priors to guide topic discovery and modeling topics from a multilingual corpus.
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