방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 설명 가능한 NMF 토픽 모델× | 설명 가능한 LDA 토픽 모델× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001 (NMF); XAI integration ~2017–present | 2003 (LDA); 2018–present (explainability extensions) |
| 창시자≠ | Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016 | Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors |
| 유형≠ | Interpretable unsupervised topic model | Probabilistic generative topic model with interpretability enhancements |
| 원전≠ | 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 ↗ |
| 별칭 | XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling | Explainable LDA, Interpretable LDA, XAI-LDA, Transparent 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. | Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery. |
| ScholarGate데이터셋 ↗ |
|
|