方法对比
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| 基于NMF主题模型的迁移学习× | LDA主题模型× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 (transfer learning survey); 1999 (NMF) | 2003 |
| 提出者≠ | Pan, S. J. & Yang, Q. (transfer learning framework); Lee, D. D. & Seung, H. S. (NMF base) | Blei, D. M., Ng, A. Y., & Jordan, M. I. |
| 类型≠ | Unsupervised topic model with cross-domain adaptation | Probabilistic generative topic model |
| 开创性文献≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| 别名 | TL-NMF, NMF transfer topic model, cross-domain NMF topic modeling, domain-adaptive NMF | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model |
| 相关 | 5 | 5 |
| 摘要≠ | Transfer Learning with NMF Topic Model applies knowledge from a labeled or data-rich source domain to improve Non-Negative Matrix Factorization topic discovery in a low-resource target domain. By initializing or constraining the NMF basis matrix with source-domain topics, the model discovers coherent target topics even when target-domain documents are scarce or unlabeled. | Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words. |
| ScholarGate数据集 ↗ |
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