Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Multigrid
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Humans perform semi-supervised classification too
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Multilevel algorithms for partitioning power-law graphs
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Graph based semi-supervised learning with sharper edges
ECML'06 Proceedings of the 17th European conference on Machine Learning
Manifold-ranking based retrieval using k-regular nearest neighbor graph
Pattern Recognition
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The recent years have witnessed a surge of interest in graph-based semi-supervised learning methods. The common denominator of these methods is that the data are represented by the nodes of a graph, the edges of which encode the pairwise similarities of the data. Despite the theoretical and empirical success, these methods have one major bottleneck which is the high computational complexity (since they usually need to solve a large-scale linear system of equations). In this paper, we propose a multilevel scheme for speeding up the traditional graph based semi-supervised learning methods. Unlike other accelerating approaches based on pure mathematical derivations (like conjugate gradient descent and Lanczos iteration) or intuitions, our method (1) has explicit physical meanings with some graph intuitions; (2) has guaranteed performance since it is closely related to the algebraic multigrid methods. Finally experimental results are presented to show the effectiveness of our method.