So sánh phương pháp

	Mô hình Chủ đề NMF Bán giám sát	Mô hình Chủ đề LDA
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2001 (NMF); semi-supervised variants from ~2010s	2003
Người khởi xướng	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Loại	Matrix factorization with supervision	Probabilistic generative topic model
Công trình gốc	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.
Tên gọi khác	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Liên quan	6	5
Tóm tắt	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.