השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל נושאי NMF בר-הסבר× | מודל נושאים LDA ניתן להסבר× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2001 (NMF); XAI integration ~2017–present | 2003 (LDA); 2018–present (explainability extensions) |
| הוגה השיטה≠ | 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 |
| סוג≠ | Interpretable unsupervised topic model | Probabilistic generative topic model with interpretability enhancements |
| מקור מכונן≠ | 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 ↗ |
| כינויים | XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling | Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model |
| קשורות≠ | 6 | 4 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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