ArticleStatistics and computing2026
Non-negative matrix factorization algorithms generally improve topic model fits.
Article in Statistics and computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Single-cell analysis of chromatin accessibility in the human intestine identifies regulatory programs and clarifies genetic associations in Crohn's disease.Nature genetics · 2026Article
- Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.Cell reports methods · 2026Article
- Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.NAR genomics and bioinformatics · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility.bioRxiv : the preprint server for biology · 2026Article
- Impact of disease-associated chromatin accessibility QTLs across immune cell types and contexts.Cell genomics · 2026Article
- Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.bioRxiv : the preprint server for biology · 2025Article
- SEEK-VEC: Robust Latent Structure Discovery via Ensemble Topic Modeling.bioRxiv : the preprint server for biology · 2025Article
- SpaTM: topic models for inferring spatially informed transcriptional programs.Briefings in bioinformatics · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids.Genome research · 2025Article
- Article
- Coronavirus research topics, tracking twenty years of research.Scientific data · 2025Article
- Review
- Deep topic modeling of spatial transcriptomics in the rheumatoid arthritis synovium identifies distinct classes of ectopic lymphoid structures.bioRxiv : the preprint server for biology · 2025Article
- Covariate-moderated Empirical Bayes Matrix Factorization.Advances in neural information processing systems · 2025Article
- An adaptive method for determining the optimal number of topics in topic modeling.PeerJ. Computer science · 2025Article
- Cell type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures.Cell genomics · 2024Article
- Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP.Nature methods · 2024Article
- A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.Life science alliance · 2024Article
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4 authors.
Funding
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".
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.