Salīdzināt metodes

	Skaidrojams NMF tēmu modelis	NMF tēmu modelis
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2001 (NMF); XAI integration ~2017–present	1999
Autors	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Lee, D. D. & Seung, H. S.
Tips	Interpretable unsupervised topic model	Matrix factorization / unsupervised topic model
Pirmavots	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Citi nosaukumi	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Saistītās	6	4
Kopsavilkums	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.