Comparer des méthodes

	Latent Dirichlet Allocation (LDA)	Factorisation de Matrices Non-Négatives (NMF)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Latent structure	Latent structure
Année d'origine	2003	1999
Auteur d'origine	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	Lee, D. D. & Seung, H. S.
Type	Generative probabilistic topic model (three-level hierarchical Bayesian)	Matrix decomposition with non-negativity constraints
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, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Apparentées	3	4
Résumé	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.