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Sparse Recovery with Graph Constraints: Fundamental Limits and
Measurement Construction
release_7czligglhffj3bzyoub2mstxbq
by
Meng Wang, Weiyu Xu, Enrique Mallada, Ao Tang
Released
as a article
.
2011
Abstract
This paper addresses the problem of sparse recovery with graph constraints in
the sense that we can take additive measurements over nodes only if they induce
a connected subgraph. We provide explicit measurement constructions for several
special graphs. A general measurement construction algorithm is also proposed
and evaluated. For any given graph G with n nodes, we derive order optimal
upper bounds of the minimum number of measurements needed to recover any
k-sparse vector over G (M^G_k,n). Our study suggests that M^G_k,n
may serve as a graph connectivity metric.
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1108.0443v1
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