Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implementing weighted b-matching algorithms: insights from a computational study
Journal of Experimental Algorithmics (JEA)
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Social content matching in MapReduce
Proceedings of the VLDB Endowment
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Dense Neighborhoods on Affinity Graph
International Journal of Computer Vision
A distributed algorithm for large-scale generalized matching
Proceedings of the VLDB Endowment
Single network relational transductive learning
Journal of Artificial Intelligence Research
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We propose preprocessing spectral clustering with b-matching to remove spurious edges in the adjacency graph prior to clustering. B-matching is a generalization of traditional maximum weight matching and is solvable in polynomial time. Instead of a permutation matrix, it produces a binary matrix with rows and columns summing to a positive integer b. The b-matching procedure prunes graph edges such that the in-degree and out-degree of each node is b, producing a more balanced variant of k-nearest-neighbor. The combinatorial algorithm optimally solves for the maximum weight subgraph and makes subsequent spectral clustering more stable and accurate. Experiments on standard datasets, visualizations, and video data support the use of b-matching to prune graphs prior to spectral clustering.