Порівняння методів

	Самокерований тематичний модель NMF	Розподіл Діріхле для прихованих тем (LDA)
Галузь	Глибоке навчання	Машинне навчання
Родина	Machine learning	Latent structure
Рік появи	2020–2022	2003
Автор методу	Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Тип	Unsupervised / self-supervised topic model	Generative probabilistic topic model (three-level hierarchical Bayesian)
Основоположне джерело	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Інші назви	SS-NMF, self-supervised topic modeling, NMF with self-supervised signals, contrastive NMF topic model	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Пов'язані	2	3
Підсумок	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.	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.