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| Aprenentatge per Transferència amb Model de Tòpics NMF× | Model de tema NMF× | |
|---|---|---|
| Camp | Aprenentatge profund | Aprenentatge profund |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2010 (transfer learning survey); 1999 (NMF) | 1999 |
| Autor original≠ | Pan, S. J. & Yang, Q. (transfer learning framework); Lee, D. D. & Seung, H. S. (NMF base) | Lee, D. D. & Seung, H. S. |
| Tipus≠ | Unsupervised topic model with cross-domain adaptation | Matrix factorization / unsupervised topic model |
| Font seminal≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| Àlies | TL-NMF, NMF transfer topic model, cross-domain NMF topic modeling, domain-adaptive NMF | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| Relacionats≠ | 5 | 4 |
| Resum≠ | Transfer Learning with NMF Topic Model applies knowledge from a labeled or data-rich source domain to improve Non-Negative Matrix Factorization topic discovery in a low-resource target domain. By initializing or constraining the NMF basis matrix with source-domain topics, the model discovers coherent target topics even when target-domain documents are scarce or unlabeled. | 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. |
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