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» On the Vulnerability of Large Graphs
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CVPR
2005
IEEE
16 years 8 months ago
Graph Embedding: A General Framework for Dimensionality Reduction
In the last decades, a large family of algorithms supervised or unsupervised; stemming from statistic or geometry theory have been proposed to provide different solutions to the p...
Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zha...
ICCV
2007
IEEE
16 years 8 months ago
Capacity Scaling for Graph Cuts in Vision
Capacity scaling is a hierarchical approach to graph representation that can improve theoretical complexity and practical efficiency of max-flow/min-cut algorithms. Introduced by ...
Olivier Juan, Yuri Boykov
ECCV
2006
Springer
16 years 8 months ago
Comparison of Energy Minimization Algorithms for Highly Connected Graphs
Algorithms for discrete energy minimization play a fundamental role for low-level vision. Known techniques include graph cuts, belief propagation (BP) and recently introduced tree-...
Vladimir Kolmogorov, Carsten Rother
ISSTA
2009
ACM
16 years 1 months ago
Identifying bug signatures using discriminative graph mining
Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Al...
Hong Cheng, David Lo, Yang Zhou, Xiaoyin Wang, Xif...
SENSYS
2003
ACM
15 years 12 months ago
GEM: graph eMbedding for routing and data-centric storage in sensor networks without geographic information
The widespread deployment of sensor networks is on the horizon. One of the main challenges in sensor networks is to process and aggregate data in the network rather than wasting e...
James Newsome, Dawn Xiaodong Song