Process / pipeline
非负矩阵分解主题模型
非负矩阵分解（NMF）主题模型利用由 Lee and Seung (1999) 提出的非负矩阵分解——一种基于部分的分解——从语料库中提取文档-主题分布。通过将文档-词项矩阵分解为两个非负矩阵，它可以恢复出一小组主题，并且倾向于比 LDA 产生更易于解释的主题。
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来源
- Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791. DOI: 10.1038/44565 ↗
- Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y. & Zhu, M. (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. Proceedings of the 30th International Conference on Machine Learning (ICML), 280-288. link ↗
如何引用本页
ScholarGate. (2026, June 1). Topic Modeling with Non-negative Matrix Factorization. ScholarGate. https://scholargate.app/zh/text-mining/topic-modeling-nmf
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