Salīdzināt metodes

	Pašuzraudzības NMF tēmu modelis	Neatūru matricas faktorizācija (NMF)
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Latent structure
Izcelsmes gads	2020–2022	1999
Autors	Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)	Lee, D. D. & Seung, H. S.
Tips	Unsupervised / self-supervised topic model	Matrix decomposition with non-negativity constraints
Pirmavots	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.
Citi nosaukumi	SS-NMF, self-supervised topic modeling, NMF with self-supervised signals, contrastive NMF topic model	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Saistītās	2	4
Kopsavilkums	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.