Porovnať metódy

	Modelovanie tém	NMF Topic Model
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	1999–2003	1999
Tvorca	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)	Lee, D. D. & Seung, H. S.
Typ	Unsupervised generative probabilistic model	Matrix factorization / unsupervised topic model
Pôvodný zdroj	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Ďalšie názvy	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Príbuzné	5	4
Zhrnutie	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.	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.