Comparer des méthodes

	Modèle LDA affiné	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2003 (base); adaptation practice ~2010s	1999
Auteur d'origine	Blei, D. M., Ng, A. Y., & Jordan, M. I. (base LDA); domain adaptation via online/warm-start LDA	Lee, D. D. & Seung, H. S.
Type	Probabilistic generative topic model (fine-tuned / domain-adapted)	Matrix factorization / unsupervised topic model
Source fondatrice	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	Domain-Adapted LDA, Adapted LDA, LDA Fine-Tuning, Online LDA Fine-Tuning	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	5	4
Résumé	Fine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather than fitting LDA from scratch, the pre-trained topic-word distributions are used as an informed starting point, enabling the model to discover coherent domain topics faster and with less data than training cold.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.