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Title: Sparse Recovery over Graph Incidence Matrices
Classical results in sparse representation guarantee the exact recovery of sparse signals under assumptions on the dictionary that are either too strong or NP hard to check. Moreover, such results may be too pessimistic in practice since they are based on a worst-case analysis. In this paper, we consider the sparse recovery of signals defined over a graph, for which the dictionary takes the form of an incidence matrix. We show that in this case necessary and sufficient conditions can be derived in terms of properties of the cycles of the graph, which can be checked in polynomial time. Our analysis further allows us to derive location dependent conditions for recovery that only depend on the cycles of the graph that intersect this support. Finally, we exploit sparsity properties on the measurements to a specialized sub-graph-based recovery algorithm that outperforms the standard $l_1$-minimization.  more » « less
Award ID(s):
1736448
NSF-PAR ID:
10078994
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
57th IEEE Conference on Decision and Control (CDC)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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