Machine learningDeep learning / NLP / CV
半监督NMF主题模型
半监督非负矩阵分解（NMF）主题模型通过整合用户提供的种子词或标签约束来扩展无监督NMF，以引导发现的主题朝向领域相关的主题。它将文档-词语矩阵分解为可解释的非负分量，同时尊重词汇先验，即使在适度的语料库中也能产生连贯的、与应用对齐的主题。
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来源
- Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗
- Jagarlamudi, J., Daume, H., & Udupa, R. (2012). Incorporating lexical priors into topic models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), 204–213. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Non-negative Matrix Factorization Topic Model. ScholarGate. https://scholargate.app/zh/deep-learning/semi-supervised-nmf-topic-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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