Sparse disjointed recovery from noninflating measurements Academic Article

abstract

• 2015 Elsevier Inc. All rights reserved. We investigate the minimal number of linear measurements needed to recover sparse disjointed vectors robustly in the presence of measurement error. First, we analyze an iterative hard thresholding algorithm relying on a dynamic program computing sparse disjointed projections to upper-bound the order of the minimal number of measurements. Next, we show that this order cannot be reduced by any robust algorithm handling noninflating measurements. As a consequence, we conclude that there is no benefit in knowing the simultaneity of sparsity and disjointedness over knowing only one of these structures.

authors

• Foucart, Simon

published proceedings

• APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS

author list (cited authors)

• Foucart, S., Minner, M. F., & Needham, T.

citation count

• 2

complete list of authors

• Foucart, Simon||Minner, Michael F||Needham, Tom

publication date

• January 2015

publisher

• Elsevier  Publisher

published in

• Applied and Computational Harmonic Analysis  Journal

keywords

• Compressive Sensing
• Disjointed Vectors
• Dynamic Programming
• Iterative Hard Thresholding
• Restricted Isometry Property
• Simultaneously Structured Models
• Sparse Vectors
• Subgaussian Matrices

Digital Object Identifier (DOI)

• 10.1016/j.acha.2015.04.005

start page

• 558

end page

• 567

volume

• 39

issue

• 3

URL

• http://dx.doi.org/10.1016/j.acha.2015.04.005