قارن الطرق

	نموذج موضوعات التخصيم المصفوفي غير السالب القابل للتفسير	نموذج مواضيع LDA
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2001 (NMF); XAI integration ~2017–present	2003
صاحب الطريقة	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Blei, D. M., Ng, A. Y., & Jordan, M. I.
النوع	Interpretable unsupervised topic model	Probabilistic generative topic model
المصدر التأسيسي	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.
الأسماء البديلة	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
ذات صلة	6	5
الملخص	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.