Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model learning have been based on a maximum likelihood objective. Efficient algorithms exist that attempt to approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds on the quality of the recovered topics, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this work we present an algorithm for learning topic models that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster. Click here to read paper

Moore Sloan Poster Paper Research Project