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» Unsupervised Learning for Graph Matching
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ECML
2006
Springer
13 years 10 months ago
B-Matching for Spectral Clustering
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 maxi...
Tony Jebara, Vlad Shchogolev
AUSAI
2008
Springer
13 years 10 months ago
Learning a Generative Model for Structural Representations
Abstract. Graph-based representations have been used with considercess in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the ...
Andrea Torsello, David L. Dowe
ESANN
2004
13 years 10 months ago
Neural methods for non-standard data
Standard pattern recognition provides effective and noise-tolerant tools for machine learning tasks; however, most approaches only deal with real vectors of a finite and fixed dime...
Barbara Hammer, Brijnesh J. Jain
IJON
2010
121views more  IJON 2010»
13 years 6 months ago
Sample-dependent graph construction with application to dimensionality reduction
Graph construction plays a key role on learning algorithms based on graph Laplacian. However, the traditional graph construction approaches of -neighborhood and k-nearest-neighbor...
Bo Yang, Songcan Chen
INFOCOM
2010
IEEE
13 years 7 months ago
Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing
—Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions are generall...
Theus Hossmann, Thrasyvoulos Spyropoulos, Franck L...