Salīdzināt metodes

	Pašuzraudzītā tekstu tēmu modelēšana	NMF tēmu modelis
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020–2023	1999
Autors	Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023)	Lee, D. D. & Seung, H. S.
Tips	Self-supervised neural topic model	Matrix factorization / unsupervised topic model
Pirmavots	Wu, X., Li, C., Zhu, Y., & Miao, Y. (2023). Effective Neural Topic Modeling with Embedding Clustering Regularization. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202, 37335–37357.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Citi nosaukumi	SSL topic model, self-supervised neural topic model, contrastive topic modeling, self-supervised LM-based topic modeling	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Saistītās	5	4
Kopsavilkums	Self-supervised topic modeling combines the interpretable topic discovery of classical topic models with self-supervised learning objectives — such as contrastive loss, masked language modeling, or reconstruction — to learn coherent, semantically rich topics from unlabeled text without human-annotated labels. It bridges classical probabilistic topic models and modern representation learning, yielding topics better aligned with contextual meaning.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.