مقایسهٔ روش‌ها

	مدل موضوعی NMF نیمه‌نظارت‌شده	مدل موضوعی LDA
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2001 (NMF); semi-supervised variants from ~2010s	2003
پدیدآور	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others	Blei, D. M., Ng, A. Y., & Jordan, M. I.
نوع	Matrix factorization with supervision	Probabilistic generative topic model
منبع بنیادین	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
نام‌های دیگر	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
مرتبط	6	5
خلاصه	Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.	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.