So sánh phương pháp

	Mô hình Chủ đề NMF Có thể Giải thích	Mô hình Chủ đề NMF
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2001 (NMF); XAI integration ~2017–present	1999
Người khởi xướng	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Lee, D. D. & Seung, H. S.
Loại	Interpretable unsupervised topic model	Matrix factorization / unsupervised topic model
Công trình gốc	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.
Tên gọi khác	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Liên quan	6	4
Tóm tắt	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.