Weak RIP and Its Application to Compressed Sensing
Journal of Advances in Mathematics and Computer Science · pp. 674–684 · Published 3 Dec 2013
10.9734/BJMCS/2014/7298Abstract
The first purpose of this paper is to give a sufficient condition under which A obeys the weak RIP and to evaluate the solution of CS using this result. The second is to show that when an m x n random matrix A satisfies the isotropy property: for every row vector A{k} of A, always obeys the weak RIP with high probability and it is applicable to the CS theory.
Cited by 1
Hiroshi Inoue · Applied and Computational Harmonic Analysis · 2014
Related research
- A Note on Guaranteed Stable Recovery of Sparse Signal in Compressed Sensing via the RIP of Orders — shares topic coverage
- Stable Recovery of Sparse Signal in Compressed Sensing via the RIP of Order less than s — shares topic coverage
- Enhancing Compressed Sensing with Graph Structural Constraints: A Novel Approach to Active Learning in Measurement Matrices — shares topic coverage
- New Bounds for Restricted Isometry Constant for the s-sparse Recovery via Compressed Sensing — shares topic coverage
- Sufficient Conditions for CS-recovery — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
1
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.