Anchors regularized: Adding robustness and extensibility to scalable topic-modeling algorithms

Abstract

Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expectation maximization. However, these new methods lack the rich priors associated with probabilistic models. We examine Arora et al.'s anchor words algorithm for topic modeling and develop new, regularized algorithms that not only mathematically resemble Gaussian and Dirichlet priors but also improve the interpretability of topic models. Our new regularization approaches make these efficient algorithms more flexible; we also show that these methods can be combined with informed priors. © 2014 Association for Computational Linguistics.