방법 비교

	Self-supervised NMF Topic Model	음이 아닌 행렬 분해(NMF)
분야	딥러닝	머신러닝
계열	Machine learning	Latent structure
기원 연도	2020–2022	1999
창시자	Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)	Lee, D. D. & Seung, H. S.
유형	Unsupervised / self-supervised topic model	Matrix decomposition with non-negativity constraints
원전	Shi, T., Guo, X., Lv, J., & Yu, P. S. (2022). Self-supervised NMF-based graph contrastive learning for semi-supervised node classification. In Proceedings of the 36th AAAI Conference on Artificial Intelligence.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
별칭	SS-NMF, self-supervised topic modeling, NMF with self-supervised signals, contrastive NMF topic model	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
관련	2	4
요약	The Self-supervised NMF Topic Model extends classical Non-negative Matrix Factorization for topic discovery by incorporating self-supervised learning signals — such as masked-word reconstruction or contrastive objectives — into the NMF optimization, yielding more coherent and semantically meaningful topics from text corpora without requiring any human-labeled data.	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.