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Non-negative matrix factorization algorithms generally improve topic model fits
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DOI：10.1007/s11222-026-10866-0.png)
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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".
Keyword:
Topic models
Non-negative matrix factorization
Nonconvex optimization
Expectation maximization
Maximum-likelihood estimation
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IF:
1.6
论文数:
200
被引数:
0
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