Salīdzināt metodes

	NMF tēmu modelis	LDA tēmu modelis
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1999	2003
Autors	Lee, D. D. & Seung, H. S.	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Tips	Matrix factorization / unsupervised topic model	Probabilistic generative topic model
Pirmavots	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Citi nosaukumi	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Saistītās	4	5
Kopsavilkums	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.	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.