Linganisha mbinu

	Uenezaji wa Lebo	Ukusanyaji wa Kikundi kwa Njia ya Spektra (Spectral Clustering)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2002	2002
Mwanzilishi	Zhu, X. & Ghahramani, Z.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Aina	Graph-based semi-supervised classification	Graph-based clustering (spectral method)
Chanzo asilia	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.
Majina mbadala	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Zinazohusiana	3	5
Muhtasari	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.