Comparer des méthodes

	Modélisation thématique multimodale	Modèle thématique NMF
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2003–present	1999
Auteur d'origine	Blei, D. M. & Jordan, M. I. (foundational corr-LDA); extended by many authors	Lee, D. D. & Seung, H. S.
Type	Generative probabilistic topic model	Matrix factorization / unsupervised topic model
Source fondatrice	Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	Multimodal LDA, multi-modal topic model, cross-modal topic modeling, MM-TM	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Apparentées	6	4
Résumé	Multimodal topic modeling discovers latent thematic structure shared across multiple data modalities — for example, co-occurring words and images — by learning a joint probabilistic representation that aligns topics across modalities. It extends classical text-only approaches such as LDA to settings where each document or observation consists of heterogeneous data types.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.