Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Skaidrojama tēmu modelēšana× | NMF tēmu modelis× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2003–2020s | 1999 |
| Autors≠ | Community practice (Blei et al. seminal; explainability extensions 2010s–present) | Lee, D. D. & Seung, H. S. |
| Tips≠ | Unsupervised topic discovery + interpretability layer | Matrix factorization / unsupervised topic model |
| Pirmavots≠ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| Citi nosaukumi | XTM, interpretable topic modeling, transparent topic modeling, explainable LDA | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team. | 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. |
| ScholarGateDatu kopa ↗ |
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