Comparer des méthodes

	Apprentissage par transfert avec modélisation thématique	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010s	1999
Auteur d'origine	Pan, S. J. & Yang, Q. (transfer learning survey); combined with Blei et al. (LDA, 2003)	Lee, D. D. & Seung, H. S.
Type	Cross-domain adaptation of topic models	Matrix factorization / unsupervised topic model
Source fondatrice	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	domain-transfer topic modeling, pretrained topic transfer, cross-domain topic adaptation, TL-LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	5	4
Résumé	Transfer Learning with Topic Modeling adapts topic structures discovered on a large or well-labeled source corpus to a related but distinct target domain where labeled data or large corpora are scarce. By reusing source-domain topic priors or pretrained embeddings as initialization, the approach produces richer, more coherent topics in the target domain than training from scratch.	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.