eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2012-02-24
230
241
10.4230/LIPIcs.STACS.2012.230
article
Efficiently Decodable Compressed Sensing by List-Recoverable Codes and Recursion
Ngo, Hung Q.
Porat, Ely
Rudra, Atri
We present two recursive techniques to construct compressed sensing schemes that can be "decoded" in sub-linear time. The first technique is based on the well studied code composition method called code concatenation where the "outer" code has strong list recoverability properties. This technique uses only one level of recursion and critically uses the power of list recovery. The second recursive technique is conceptually similar, and has multiple recursion levels. The following compressed sensing results are obtained using these techniques:
- Strongly explicit efficiently decodable l_1/l_1 compressed sensing matrices: We present a strongly explicit ("for all") compressed sensing measurement matrix with O(d^2log^2 n) measurements that can output near-optimal d-sparse approximations in time poly(d log n).
- Near-optimal efficiently decodable l_1/l_1 compressed sensing matrices for non-negative signals: We present two randomized constructions of ("for all") compressed sensing matrices with near optimal number of measurements: O(d log n loglog_d n) and O_{m,s}(d^{1+1/s} log n (log^(m) n)^s), respectively, for any integer parameters s,m>=1. Both of these constructions can output near optimal d-sparse approximations for non-negative signals in time poly(d log n).
To the best of our knowledge, none of the results are dominated by existing results in the literature.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol014-stacs2012/LIPIcs.STACS.2012.230/LIPIcs.STACS.2012.230.pdf
Compressed Sensing
Sub-Linear Time Decoding
List-Recoverable Codes