Latent Dirichlet analysis, or topic modeling, is a flexible latent variable
framework for modeling high-dimensional sparse count data. Various learning
algorithms have been developed in recent years, including collapsed Gibbs
sampling, variational inference, and maximum a posteriori estimation, and this
variety motivates the need for careful empirical comparisons. In this paper, we
highlight the close connections between these approaches. We find that the main
differences are attributable to the amount of smoothing applied to the counts.
When the hyperparameters are optimized, the differences in performance among
the algorithms diminish significantly. The ability of these algorithms to
achieve solutions of comparable accuracy gives us the freedom to select
computationally efficient approaches. Using the insights gained from this
comparative study, we show how accurate topic models can be learned in several
seconds on text corpora with thousands of documents.

本文探讨了用于高维稀疏计数数据建模的潜在狄利克雷分析或话题模型，比较了多种学习算法，发现主要区别在于应用于计数的平滑量。在优化超参数时，算法性能的差异显著缩小，这使得我们能够选择计算效率高的方法来学习准确的主题模型。