document: 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 techniques 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 documents 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 multiplication . In addition to these inference methods , we present SparseLDA , an algorithm and data structure for evaluating 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 substantially less memory .
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