Linganisha mbinu

	Mfumo wa Mfumo wa Mada wa NMF	Mfumo wa Mada wa LDA
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	1999	2003
Mwanzilishi	Lee, D. D. & Seung, H. S.	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Aina	Matrix factorization / unsupervised topic model	Probabilistic generative topic model
Chanzo asilia	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.
Majina mbadala	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Zinazohusiana	4	5
Muhtasari	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.