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SIAMJO
2010
87views more  SIAMJO 2010»
13 years 2 months ago
Universal Rigidity and Edge Sparsification for Sensor Network Localization
Owing to their high accuracy and ease of formulation, there has been great interest in applying convex optimization techniques, particularly that of semidefinite programming (SDP)...
Zhisu Zhu, Anthony Man-Cho So, Yinyu Ye
SODA
2012
ACM
213views Algorithms» more  SODA 2012»
11 years 10 months ago
Expanders are universal for the class of all spanning trees
Given a class of graphs F, we say that a graph G is universal for F, or F-universal, if every H ∈ F is contained in G as a subgraph. The construction of sparse universal graphs ...
Daniel Johannsen, Michael Krivelevich, Wojciech Sa...
CORR
2011
Springer
183views Education» more  CORR 2011»
13 years 2 months ago
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
— We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally corre...
Zhilin Zhang, Bhaskar D. Rao
CVPR
2009
IEEE
15 years 2 months ago
Minimizing Sparse Higher Order Energy Functions of Discrete Variables
Higher order energy functions have the ability to encode high level structural dependencies between pixels, which have been shown to be extremely powerful for image labeling pro...
Carsten Rother (Microsoft Research Cambridge), Pus...
TSP
2010
13 years 2 months ago
Double sparsity: learning sparse dictionaries for sparse signal approximation
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
Ron Rubinstein, Michael Zibulevsky, Michael Elad