So sánh phương pháp

	Mô hình hóa chủ đề bán giám sát	Phân tích ma trận không âm (NMF)
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Latent structure
Năm ra đời	2009	1999
Người khởi xướng	Ramage, D.; Andrzejewski, D.; and related NLP community	Lee, D. D. & Seung, H. S.
Loại	Probabilistic graphical model (supervised/constrained extension of LDA)	Matrix decomposition with non-negativity constraints
Công trình gốc	Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Tên gọi khác	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Liên quan	3	4
Tóm tắt	Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for statistical strength.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.