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» Algorithms for Large, Sparse Network Alignment Problems
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ICIP
2009
IEEE
14 years 8 months ago
Sparsity And Morphological Diversity For Hyperspectral Data Analysis
Recently morphological diversity and sparsity have emerged as new and effective sources of diversity for Blind Source Separation. Based on these new concepts, novel methods such a...
NIPS
2004
13 years 9 months ago
VDCBPI: an Approximate Scalable Algorithm for Large POMDPs
Existing algorithms for discrete partially observable Markov decision processes can at best solve problems of a few thousand states due to two important sources of intractability:...
Pascal Poupart, Craig Boutilier
PAMI
2011
13 years 2 months ago
Approximately Global Optimization for Robust Alignment of Generalized Shapes
—In this paper, we introduce a novel method to solve shape alignment problems. We use gray-scale “images” to represent source shapes, and propose a novel two-component Gaussi...
Hongsheng Li, Tian Shen, Xiaolei Huang
CORR
2010
Springer
228views Education» more  CORR 2010»
13 years 6 months ago
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
TWC
2008
133views more  TWC 2008»
13 years 7 months ago
Beam Selection Strategies for Orthogonal Random Beamforming in Sparse Networks
Abstract--Orthogonal random beamforming (ORB) constitutes a mean to exploit spatial multiplexing and multi-user diversity (MUD) gains in multi-antenna broadcast channels. To do so,...
José López Vicario, Roberto Bosisio,...