Vertaile menetelmiä
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| Selitettävä NMF-aihemalli× | Latent Dirichlet Allocation (LDA) -aiheiden malli× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2001 (NMF); XAI integration ~2017–present | 2003 |
| Kehittäjä≠ | Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016 | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| Tyyppi≠ | Interpretable unsupervised topic model | Probabilistic generative topic model |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
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