방법 비교

	준지도 학습 토픽 모델링	음이 아닌 행렬 분해(NMF)
분야	딥러닝	머신러닝
계열	Machine learning	Latent structure
기원 연도	2009	1999
창시자	Ramage, D.; Andrzejewski, D.; and related NLP community	Lee, D. D. & Seung, H. S.
유형	Probabilistic graphical model (supervised/constrained extension of LDA)	Matrix decomposition with non-negativity constraints
원전	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.
별칭	semi-supervised LDA, labeled LDA, seed-guided topic modeling, constrained topic model	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
관련	3	4
요약	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.