Porovnať metódy

	Vysvetliteľný NMF model tém	Vysvetliteľný model tém LDA
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2001 (NMF); XAI integration ~2017–present	2003 (LDA); 2018–present (explainability extensions)
Tvorca	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
Typ	Interpretable unsupervised topic model	Probabilistic generative topic model with interpretability enhancements
Pôvodný zdroj	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Ďalšie názvy	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model
Príbuzné	6	4
Zhrnutie	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.