Võrdle meetodeid
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| Siltide levitamine× | Spektraalklasterdamine× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta | 2002 | 2002 |
| Looja≠ | Zhu, X. & Ghahramani, Z. | Ng, A. Y.; Jordan, M. I.; Weiss, Y. |
| Tüüp≠ | Graph-based semi-supervised classification | Graph-based clustering (spectral method) |
| Algallikas≠ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856. link ↗ |
| Rööpnimetused≠ | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation | NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering |
| Seotud≠ | 3 | 5 |
| Kokkuvõte≠ | 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. | Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate. |
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