So sánh phương pháp

	Mô hình Chủ đề NMF Bán giám sát	Topic Modeling (Mô hình hóa chủ đề)
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2001 (NMF); semi-supervised variants from ~2010s	1999–2003
Người khởi xướng	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Loại	Matrix factorization with supervision	Unsupervised generative probabilistic 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	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
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.	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.