Methoden vergleichen
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| Selbstüberwachtes Zufallswald× | Label Propagation× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2012–2022 | 2002 |
| Urheber≠ | Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage) | Zhu, X. & Ghahramani, Z. |
| Typ≠ | Semi-supervised ensemble (self-supervised pretext task + RF) | Graph-based semi-supervised classification |
| Wegweisende Quelle≠ | Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430. 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 ↗ |
| Aliasnamen | SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Verwandt≠ | 6 | 3 |
| Zusammenfassung≠ | Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees. | 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. |
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