Comparer des méthodes

	Modèle de Topics LDA Auto-supervisé	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2003 (LDA); self-supervised variants from 2020	1999
Auteur d'origine	Blei, D. M., Ng, A. Y., Jordan, M. I. (LDA); self-supervised extension by multiple authors (2020s)	Lee, D. D. & Seung, H. S.
Type	Probabilistic generative model with self-supervised pretraining	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	SSL-LDA, self-supervised topic modeling, self-supervised LDA, contrastive LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	6	4
Résumé	Self-supervised LDA combines the probabilistic generative framework of Latent Dirichlet Allocation with self-supervised pretraining signals — such as masked-word prediction or contrastive document objectives — to guide topic discovery without requiring hand-labeled training data. The result is topic representations that are simultaneously grounded in distributional statistics and enriched by language structure learned from raw text.	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.