Efficient and Robust Compressed Sensing Using Optimized Expander Graphs

DOI10.1109/TIT.2009.2025528zbMath1367.94095OpenAlexW2140466267WikidataQ56474543 ScholiaQ56474543MaRDI QIDQ4973998

Robert Calderbank, Sina Jafarpour, Weiyu Xu, Babak Hassibi

Publication date: 8 August 2017

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tit.2009.2025528

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Detection theory in information and communication theory (94A13) Connectivity (05C40) Sampling theory in information and communication theory (94A20)

Related Items (11)

A Novel Compressed Sensing Scheme for Photoacoustic Tomography ⋮ Expander \(\ell_0\)-decoding ⋮ Strengthening hash families and compressive sensing ⋮ Simple Codes and Sparse Recovery with Fast Decoding ⋮ A hierarchical framework for recovery in compressive sensing ⋮ Sampling in the analysis transform domain ⋮ Discrete optimization methods for group model selection in compressed sensing ⋮ Compressed sensing with sparse binary matrices: instance optimal error guarantees in near-optimal time ⋮ Randomization of data acquisition and \(\ell_{1}\)-optimization (recognition with compression) ⋮ Peeling decoding of LDPC codes with applications in compressed sensing ⋮ Deterministic construction of sparse binary matrices via incremental integer optimization

This page was built for publication: Efficient and Robust Compressed Sensing Using Optimized Expander Graphs