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| 설명 가능한 NMF 토픽 모델× | NMF 토픽 모델× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001 (NMF); XAI integration ~2017–present | 1999 |
| 창시자≠ | Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016 | Lee, D. D. & Seung, H. S. |
| 유형≠ | Interpretable unsupervised topic model | Matrix factorization / unsupervised topic model |
| 원전≠ | Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| 별칭 | XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| 관련≠ | 6 | 4 |
| 요약≠ | 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. | 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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