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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Date   2011-08-01
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arXiv  1108.0443v1
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