方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 自监督NMF主题模型× | 潜在狄利克雷分配 (LDA)× | |
|---|---|---|
| 领域≠ | 深度学习 | 机器学习 |
| 方法族≠ | Machine learning | Latent structure |
| 起源年份≠ | 2020–2022 | 2003 |
| 提出者≠ | Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022) | Blei, D. M.; Ng, A. Y.; Jordan, M. I. |
| 类型≠ | Unsupervised / self-supervised topic model | Generative probabilistic topic model (three-level hierarchical Bayesian) |
| 开创性文献≠ | 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 ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. DOI ↗ |
| 别名≠ | SS-NMF, self-supervised topic modeling, NMF with self-supervised signals, contrastive NMF topic model | LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling |
| 相关≠ | 2 | 3 |
| 摘要≠ | The Self-supervised NMF Topic Model extends classical Non-negative Matrix Factorization for topic discovery by incorporating self-supervised learning signals — such as masked-word reconstruction or contrastive objectives — into the NMF optimization, yielding more coherent and semantically meaningful topics from text corpora without requiring any human-labeled data. | Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing. |
| ScholarGate数据集 ↗ |
|
|