Efficient methods for topic model inference on streaming document collections

Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents requires approximate inference tech-niques that are computationally expensive. With today’s large-scale, constantly expanding document collections, it is useful to be able to infer topic distributions for new doc-uments without retraining the model. In this paper, we empirically evaluate the performance of several methods for topic inference in previously unseen documents, including methods based on Gibbs sampling, variational inference, and a new method inspired by text classification. The classification-based inference method produces results similar to iterative inference methods, but requires only a single matrix multi-plication. In addition to these inference methods, we present SparseLDA, an algorithm and data structure for evaluat-ing Gibbs sampling distributions. Empirical results indicate that SparseLDA can be approximately 20 times faster than traditional LDA and provide twice the speedup of previously published fast sampling methods, while also using substan-tially less memory