So sánh phương pháp

	Mô hình Chủ đề NMF	Mô hình Chủ đề LDA
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	1999	2003
Người khởi xướng	Lee, D. D. & Seung, H. S.	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Loại	Matrix factorization / unsupervised topic model	Probabilistic generative topic model
Công trình gốc	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.
Tên gọi khác	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Liên quan	4	5
Tóm tắt	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.