NDLI: Compressive sensing over graphs

Content Provider	IEEE Xplore Digital Library
Author	Weiyu Xu Mallada, E. Ao Tang
Copyright Year	2011
Description	Author affiliation: Cornell University, Ithaca, NY 14853, USA (Weiyu Xu; Mallada, E.; Ao Tang)
Abstract	In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing compressive sensing results, the collective additive measurements we are allowed to take must follow connected paths over the underlying graph. For a sufficiently connected graph with n nodes, it is shown that, using O(k log(n)) path measurements, we are able to recover any k-sparse link vector (with no more than k nonzero elements), even though the measurements have to follow the graph path constraints. We mainly show that the computationally efficient ℓ1 minimization can provide theoretical guarantees for inferring such k-sparse vectors with O(k log(n)) path measurements from the graph.
Starting Page	2087
Ending Page	2095
File Size	472537
Page Count	9
File Format	PDF
ISBN	9781424499199
ISSN	0743166X
e-ISBN	9781424499212
DOI	10.1109/INFCOM.2011.5935018
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Compressed sensing Delay Testing Indexes Tomography Minimization Bipartite graph
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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