Methoden vergleichen

	Schwache überwachte LDA-Themenmodellierung	NMF-Themenmodell
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2009–2012	1999
Urheber	Jagarlamudi et al.; Andrzejewski et al.	Lee, D. D. & Seung, H. S.
Typ	Probabilistic generative model with weak supervision	Matrix factorization / unsupervised topic model
Wegweisende Quelle	Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pp. 204–213.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Aliasnamen	WS-LDA, Guided LDA, Seeded LDA, Constrained LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Verwandt	6	4
Zusammenfassung	Weakly Supervised LDA is an extension of Latent Dirichlet Allocation that incorporates lightweight human guidance — typically keyword seeds or must-link/cannot-link constraints — into the Dirichlet priors, steering learned topics toward domain-meaningful themes without requiring fully labeled documents. It sits between fully unsupervised LDA and supervised classification, making it well-suited to situations where labeling thousands of documents is impractical.	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.