方法对比
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| 半监督K近邻× | 标签传播× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2002 (semi-supervised extension); 1967 (KNN base) | 2002 |
| 提出者≠ | Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base) | Zhu, X. & Ghahramani, Z. |
| 类型≠ | Semi-supervised classifier / label propagation | Graph-based semi-supervised classification |
| 开创性文献≠ | Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| 别名 | SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| 相关≠ | 4 | 3 |
| 摘要≠ | Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
| ScholarGate数据集 ↗ |
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