Work

Non-negative matrix factorization algorithms generally improve topic model fits

Year: 2026

Type: article

Abstract: In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known, at least informally, that maximum-likelihood estimation in topic models is closely related to non-negative matrix factorization (NMF). Yet, to our knowledge, this relationship has not been exploited previously to fit topic models. We show that recent advances in NMF optimization methods can be leveraged to fit topic models very efficiently, often resulting in much better fits and in less time than existing algorithms for topic models. We also formally make the connection between the NMF optimization problem and maximum-likelihood estimation for the topic model, and using this result we show that the expectation maximization (EM) algorithm for the topic model is essentially the same as the classic multiplicative updates for NMF. Our methods are implemented in the R package "fastTopics".

Source: Statistics and Computing

DOI: 10.1007/s11222-026-10866-0

Authors: Peter Carbonetto, Abhishek Sarkar, Zihao Wang, Matthew Stephens

Institution: University of Chicago

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,890

Funders: National Institutes of Health, National Human Genome Research Institute