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» Kernels and Regularization on Graphs
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CVPR
2005
IEEE
16 years 1 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...
STOC
2010
ACM
220views Algorithms» more  STOC 2010»
15 years 3 months ago
Combinatorial approach to the interpolation method and scaling limits in sparse random graphs
We establish the existence of free energy limits for several sparse random hypergraph models corresponding to certain combinatorial models on Erd¨os-R´enyi graph G(N, c/N) and r...
Mohsen Bayati, David Gamarnik, Prasad Tetali
CVPR
2009
IEEE
1096views Computer Vision» more  CVPR 2009»
16 years 6 months ago
How far can you get with a modern face recognition test set using only simple features?
In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously ...
Nicolas Pinto, James J. DiCarlo, David D. Cox
DATE
2005
IEEE
109views Hardware» more  DATE 2005»
15 years 5 months ago
ISEGEN: Generation of High-Quality Instruction Set Extensions by Iterative Improvement
Customization of processor architectures through Instruction Set Extensions (ISEs) is an effective way to meet the growing performance demands of embedded applications. A high-qua...
Partha Biswas, Sudarshan Banerjee, Nikil D. Dutt, ...
EMMCVPR
2005
Springer
15 years 5 months ago
Probabilistic Subgraph Matching Based on Convex Relaxation
We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with ...
Christian Schellewald, Christoph Schnörr