Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Eneseteadlik NMF-teemamudel× | Latent Dirichlet Allocation (LDA)× | |
|---|---|---|
| Valdkond≠ | Süvaõpe | Masinõpe |
| Perekond≠ | Machine learning | Latent structure |
| Tekkeaasta≠ | 2020–2022 | 2003 |
| Looja≠ | Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022) | Blei, D. M.; Ng, A. Y.; Jordan, M. I. |
| Tüüp≠ | Unsupervised / self-supervised topic model | Generative probabilistic topic model (three-level hierarchical Bayesian) |
| Algallikas≠ | 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. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. DOI ↗ |
| Rööpnimetused≠ | 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 |
| Seotud≠ | 2 | 3 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
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