Сравнение методов

	Тематическая модель NMF с адаптацией к предметной области	Тематическое моделирование
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	1999 (NMF); domain adaptation variants ~2010s	1999–2003
Автор метода	Lee, D. D. & Seung, H. S. (NMF base); domain adaptation extensions by the NLP community	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Тип	Unsupervised topic model with domain adaptation	Unsupervised generative probabilistic model
Основополагающий источник	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.
Другие названия	DA-NMF, cross-domain NMF, domain-adaptive topic modeling with NMF, transfer NMF topic model	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Связанные	4	5
Сводка	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.