방법 비교

	도메인 적응형 NMF 토픽 모델	NMF 토픽 모델
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	1999 (NMF); domain adaptation variants ~2010s	1999
창시자	Lee, D. D. & Seung, H. S. (NMF base); domain adaptation extensions by the NLP community	Lee, D. D. & Seung, H. S.
유형	Unsupervised topic model with domain adaptation	Matrix factorization / unsupervised topic model
원전	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
별칭	DA-NMF, cross-domain NMF, domain-adaptive topic modeling with NMF, transfer NMF topic model	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
관련	4	4
요약	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.	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.