A Practical Algorithm for Topic Modeling with Provable Guarantees. Arora, S., Ge, R., Halpern, Y., Mimno, D. M., Moitra, A., Sontag, D., Wu, Y., & Zhu, M. In *Proceedings of the International Conference on Machine Learning (ICML)*, volume 28 (2), pages 280–288, 2013. JMLR: W&CP.

Paper abstract bibtex 3 downloads

Paper abstract bibtex 3 downloads

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, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper 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.

@inproceedings{AroraEtAl_icml13, author = {Sanjeev Arora and Rong Ge and Yoni Halpern and David M. Mimno and Ankur Moitra and David Sontag and Yichen Wu and Michael Zhu}, title = {A Practical Algorithm for Topic Modeling with Provable Guarantees}, booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, year = {2013}, publisher = {JMLR: W\&CP}, volume = {28 (2)}, pages = {280--288}, keywords = {Machine learning, Unsupervised learning, Topic models}, url_Paper = {http://people.csail.mit.edu/dsontag/papers/AroraEtAl_icml13.pdf}, abstract = {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, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper 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.} }

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