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Model-based Sketching and Recovery with Expanders
[chapter]
2013
Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms
Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper considers the same problem with the added twist that the sparse coefficients of the unknown vector exhibit further correlations as determined by a known sparsity model. We prove that exploiting model-based sparsity in recovery provably reduces the
doi:10.1137/1.9781611973402.112
dblp:conf/soda/BahBC14
fatcat:3h65pyybfnfnznvrugaxwldwem