Machine learningDeep learning / NLP / CV
自监督NMF主题模型
自监督NMF主题模型通过将自监督学习信号（例如掩码词重建或对比目标）整合到NMF优化中，扩展了用于主题发现的经典非负矩阵分解，从而无需任何人工标注数据即可从文本语料库中获得更连贯、语义更丰富的主题。
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来源
- Shi, T., Guo, X., Lv, J., & Yu, P. S. (2022). Self-supervised NMF-based graph contrastive learning for semi-supervised node classification. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. link ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Non-negative Matrix Factorization Topic Model. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-nmf-topic-model
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