Salīdzināt metodes

	Iezīmju izplatīšana	Spektrālā klasterizācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2002	2002
Autors	Zhu, X. & Ghahramani, Z.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tips	Graph-based semi-supervised classification	Graph-based clustering (spectral method)
Pirmavots	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	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.
Citi nosaukumi	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Saistītās	3	5
Kopsavilkums	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.