Title :
Optimal binary measurement matrices for compressed sensing
Author :
Tehrani, Arash Saber ; Dimakis, Alexandros G. ; Caire, Giuseppe
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
We explicitly construct binary measurement matrices with good sparse approximation guarantees. Specifically, our measurement matrices have an order optimal number of measurements and have ℓ1/ℓ1 approximation guarantee. Our construction uses the progressive edge growth technique. We apply coding theoretic results and rely on a recent connection of compressed sensing to LP relaxation for channel decoding.
Keywords :
channel coding; compressed sensing; linear predictive coding; matrix algebra; LP relaxation; channel decoding; coding theoretic results; compressed sensing; optimal binary measurement matrices; order optimal number; progressive edge growth technique; sparse approximation guarantees; Approximation methods; Compressed sensing; Decoding; Parity check codes; Sparse matrices; Vectors;
Conference_Titel :
Information Theory Workshop (ITW), 2013 IEEE
Conference_Location :
Sevilla
Print_ISBN :
978-1-4799-1321-3
DOI :
10.1109/ITW.2013.6691243
