So sánh phương pháp

	Mô hình Chủ đề NMF Thích ứng Miền	Mô hình Chủ đề LDA
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	1999 (NMF); domain adaptation variants ~2010s	2003
Người khởi xướng	Lee, D. D. & Seung, H. S. (NMF base); domain adaptation extensions by the NLP community	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Loại	Unsupervised topic model with domain adaptation	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	DA-NMF, cross-domain NMF, domain-adaptive topic modeling with NMF, transfer NMF topic model	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Liên quan	4	5
Tóm tắt	Domain-adaptive NMF Topic Modeling applies Non-negative Matrix Factorization to discover latent topics across text from multiple domains, using regularization or shared basis constraints to transfer topic knowledge from a resource-rich source domain to a target domain with limited labeled data. It combines interpretable parts-based decomposition with domain-adaptation objectives to produce topics that are both domain-specific and cross-domain consistent.	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.