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Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Model de tema NMF Explicable× | Model de temes LDA× | |
|---|---|---|
| Camp | Aprenentatge profund | Aprenentatge profund |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2001 (NMF); XAI integration ~2017–present | 2003 |
| Autor original≠ | Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016 | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| Tipus≠ | Interpretable unsupervised topic model | Probabilistic generative topic model |
| Font seminal≠ | Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Àlies | XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| Relacionats≠ | 6 | 5 |
| Resum≠ | An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers. | 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. |
| ScholarGateConjunt de dades ↗ |
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