Comparer des méthodes

	Modèle de Topic NMF Explicable	Modélisation par sujets
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2001 (NMF); XAI integration ~2017–present	1999–2003
Auteur d'origine	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Type	Interpretable unsupervised topic model	Unsupervised generative probabilistic model
Source fondatrice	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Apparentées	6	5
Résumé	An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.