A Practical Algorithm for Topic Modeling with Provable Guarantees
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.
Cite
Text
Arora et al. "A Practical Algorithm for Topic Modeling with Provable Guarantees." International Conference on Machine Learning, 2013.Markdown
[Arora et al. "A Practical Algorithm for Topic Modeling with Provable Guarantees." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/arora2013icml-practical/)BibTeX
@inproceedings{arora2013icml-practical,
title = {{A Practical Algorithm for Topic Modeling with Provable Guarantees}},
author = {Arora, Sanjeev and Ge, Rong and Halpern, Yonatan and Mimno, David and Moitra, Ankur and Sontag, David and Wu, Yichen and Zhu, Michael},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {280-288},
volume = {28},
url = {https://mlanthology.org/icml/2013/arora2013icml-practical/}
}