Σύγκριση μεθόδων

	Μοντέλο Θέματος NMF Αυτο-εποπτευόμενης Μάθησης	Εκχώρηση Δεσμευμένων Dirichlet (LDA)
Πεδίο	Βαθιά Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Latent structure
Έτος προέλευσης	2020–2022	2003
Δημιουργός	Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Τύπος	Unsupervised / self-supervised topic model	Generative probabilistic topic model (three-level hierarchical Bayesian)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	2	3
Σύνοψη	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.