Сравнение на методи

	Самоконтролиран NMF топик модел	Неотрицателна матрична факторизация (NMF)
Област	Дълбоко обучение	Машинно обучение
Семейство	Machine learning	Latent structure
Година на възникване	2020–2022	1999
Създател	Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)	Lee, D. D. & Seung, H. S.
Тип	Unsupervised / self-supervised topic model	Matrix decomposition with non-negativity constraints
Основополагащ източник	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.
Други названия	SS-NMF, self-supervised topic modeling, NMF with self-supervised signals, contrastive NMF topic model	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Свързани	2	4
Резюме	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.