Comparer des méthodes

	Modèle de Topics LDA	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2003	1999
Auteur d'origine	Blei, D. M., Ng, A. Y., & Jordan, M. I.	Lee, D. D. & Seung, H. S.
Type	Probabilistic generative topic model	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	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	5	4
Résumé	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.	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.