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| Model tematyczny NMF× | Model tematyczny LDA× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1999 | 2003 |
| Twórca≠ | Lee, D. D. & Seung, H. S. | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| Typ≠ | Matrix factorization / unsupervised topic model | Probabilistic generative topic model |
| Źródło pierwotne≠ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Inne nazwy | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. | Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words. |
| ScholarGateZbiór danych ↗ |
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