Comparer des méthodes

	Factorisation de Matrices Non-Négatives (NMF)	Analyse en Composantes Indépendantes (ACI)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Latent structure	Latent structure
Année d'origine	1999	1994
Auteur d'origine	Lee, D. D. & Seung, H. S.	Comon, P.
Type	Matrix decomposition with non-negativity constraints	Blind source separation / latent-structure decomposition
Source fondatrice	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.
Alias	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	ICA, blind source separation, BSS, FastICA
Apparentées	4	3
Résumé	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.	Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive, statistically independent subcomponents. Formalized by Pierre Comon in 1994, ICA became the foundational framework for blind source separation and is widely applied in neuroimaging (fMRI, EEG), speech processing, and biomedical signal analysis.