An Empirical Bayes Approach to Topic Modeling

Anirban Gangopadhyay
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Abstract

Given a corpus of documents, we consider the problem of finding latent topics, and introduce a novel Empirical Bayes based framework that allows us to choose the optimal topic modeling algorithm given observed variables in the data. We specifically consider three disparate algorithms - LDA, graph clustering, and non-negative matrix factorization - and provide a standardized framework that compares statistical and generative assumptions each algorithm makes. We then provide a model selection algorithm that quantifies each model based on how well assumptions match the data. We illustrate the efficacy of our approach by applying our framework to different sets of document corpuses and empirically measuring results.
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主题建模的经验贝叶斯方法
给定一个文档语料库,我们考虑寻找潜在主题的问题,并引入一种新的基于经验贝叶斯的框架,该框架允许我们根据数据中观察到的变量选择最佳主题建模算法。我们特别考虑了三种不同的算法——LDA、图聚类和非负矩阵分解——并提供了一个标准化的框架来比较每种算法所做的统计和生成假设。然后,我们提供了一个模型选择算法,该算法根据假设与数据的匹配程度来量化每个模型。我们通过将我们的框架应用于不同的文档语料库集和经验测量结果来说明我们方法的有效性。
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