Machine learningDeep learning / NLP / CV
域自适应非负矩阵分解主题模型
域自适应非负矩阵分解（NMF）主题建模将非负矩阵分解应用于发现来自多个领域文本的潜在主题，使用正则化或共享基约束将主题知识从资源丰富的源域转移到标记数据有限的目标域。它结合了可解释的基于部分的分解与域自适应目标，以生成既领域特定又跨领域一致的主题。
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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(6755), 788–791. DOI: 10.1038/44565 ↗
- Non-negative matrix factorization. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-Adaptive Non-negative Matrix Factorization Topic Model. ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-nmf-topic-model
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