Krahasoni metodat

	Model Tematik NMF i Shpjegueshëm	Modeli i shpjegueshëm LDA (Latent Dirichlet Allocation)
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2001 (NMF); XAI integration ~2017–present	2003 (LDA); 2018–present (explainability extensions)
Krijuesi	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
Lloji	Interpretable unsupervised topic model	Probabilistic generative topic model with interpretability enhancements
Burimi themelues	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.
Emërtime të tjera	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model
Të lidhura	6	4
Përmbledhja	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.