Method evidence record
Explainable NMF Topic Model
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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Explainable Non-negative Matrix Factorization Topic Model
Taxonomic method record · ml-model / deep-learning
- Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. · URL
- Non-negative matrix factorization. Wikipedia. · URL
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