  We report on the potential for using algorithms for non-negative matrix
factorization (NMF) to improve parameter estimation in topic models. While
several papers have studied connections between NMF and topic models, none have
suggested leveraging these connections to develop new algorithms for fitting
topic models. NMF avoids the "sum-to-one" constraints on the topic model
parameters, resulting in an optimization problem with simpler structure and
more efficient computations. Building on recent advances in optimization
algorithms for NMF, we show that first solving the NMF problem then recovering
the topic model fit can produce remarkably better fits, and in less time, than
standard algorithms for topic models. While we focus primarily on maximum
likelihood estimation, we show that this approach also has the potential to
improve variational inference for topic models. Our methods are implemented in
the R package fastTopics.
