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We report on the potential for using algorithms for non-negative matrixfactorization (NMF) to improve parameter estimation in topic models. Whileseveral papers have studied connections between NMF and topic models, none havesuggested leveraging these connections to develop new algorithms for fittingtopic models. NMF avoids the "sum-to-one" constraints on the topic modelparameters, resulting in an optimization problem with simpler structure andmore efficient computations. Building on recent advances in optimizationalgorithms for NMF, we show that first solving the NMF problem then recoveringthe topic model fit can produce remarkably better fits, and in less time, thanstandard algorithms for topic models. While we focus primarily on maximumlikelihood estimation, we show that this approach also has the potential toimprove variational inference for topic models. Our methods are implemented inthe R package fastTopics.

Non-negative matrix factorization algorithms greatly improve topic model fits