So sánh phương pháp

	Mô hình Chủ đề NMF Có thể Giải thích	Mô hình Chủ đề LDA
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2001 (NMF); XAI integration ~2017–present	2003
Người khởi xướng	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Loại	Interpretable unsupervised topic model	Probabilistic generative 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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Tên gọi khác	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Liên quan	6	5
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.	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.