Comparer des méthodes

	Factorisation de Matrices Non-Négatives (NMF)	Word2Vec
Domaine	Apprentissage automatique	Fouille de textes
Famille	Latent structure	Process / pipeline
Année d'origine	1999	2013
Auteur d'origine	Lee, D. D. & Seung, H. S.	Tomas Mikolov et al.
Type	Matrix decomposition with non-negativity constraints	Neural word-embedding model
Source fondatrice	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Apparentées	4	4
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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.