Paper Info
Title
Design of projection matrix for compressive sensing by nonsmooth optimization
Abstract
Sparsity and incoherence are the two key ingredients in compressive sensing (CS). Given a sparsifying dictionary D, the projection matrix P must be as incoherent with D as possible for the CS system to be efficient. Thus the design of projection matrix is naturally a problem of minimizing the coherence between P andD. Unfortunately, this turns out to be a nonconvex, nonsmooth, large-scale problem even for a CS system of moderate size. In this paper, the above-mentioned problem is investigated in a formulation where the problem is converted into a sequence of nonsmooth but convex subproblems. A subgradient projection algorithm is proposed to solve the nonsmooth subproblems that converges to a projection matrix with improved performance. The performance of the proposed algorithm is evaluated by simulations and comparisons with several existing techniques.
Year
DOI
Venue
2014
10.1109/ISCAS.2014.6865376
ISCAS
Keywords
Field
DocType
optimisation,projection matrix design,compressive sensing,subgradient projection algorithm,matrix algebra,compressed sensing,coherence,dictionaries,linear programming,vectors
Mathematical optimization,Dykstra's projection algorithm,Subgradient method,Computer science,Projection (linear algebra),Regular polygon,Coherence (physics),Compressed sensing
Conference
ISSN
Citations 
PageRank 
0271-4302
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Wu-Sheng Lu132949.40
Takao Hinamoto215850.09