विधियों की तुलना करें

	व्याख्या योग्य विषय मॉडलिंग	एनएमएफ विषय मॉडल
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2003–2020s	1999
प्रवर्तक	Community practice (Blei et al. seminal; explainability extensions 2010s–present)	Lee, D. D. & Seung, H. S.
प्रकार	Unsupervised topic discovery + interpretability layer	Matrix factorization / unsupervised topic model
मौलिक स्रोत	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
उपनाम	XTM, interpretable topic modeling, transparent topic modeling, explainable LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
संबंधित	6	4
सारांश	Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team.	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.