Comparer des méthodes

	Modèle thématique par factorisation matricielle non négative semi-supervisée	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2001 (NMF); semi-supervised variants from ~2010s	1999
Auteur d'origine	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others	Lee, D. D. & Seung, H. S.
Type	Matrix factorization with supervision	Matrix factorization / unsupervised topic model
Source fondatrice	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	6	4
Résumé	Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.	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.