Comparer des méthodes

	Modélisation thématique par NMF	TF-IDF
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999	1988
Auteur d'origine	Lee & Seung	Salton & Buckley
Type	Matrix-factorization topic model	Text vectorization / term-weighting scheme
Source fondatrice	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Alias	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Apparentées	4	3
Résumé	NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.