Discrete optimization methods for group model selection in compressed sensing
In this article we study the problem of signal recovery for group models. More precisely for a
given set of groups, each containing a small subset of indices, and for given linear sketches …
given set of groups, each containing a small subset of indices, and for given linear sketches …
Group-sparse model selection: Hardness and relaxations
Group-based sparsity models are instrumental in linear and non-linear regression problems.
The main premise of these models is the recovery of “interpretable” signals through the …
The main premise of these models is the recovery of “interpretable” signals through the …
Tractability of interpretability via selection of group-sparse models
Group-based sparsity models are proven instrumental in linear regression problems for
recovering signals from much fewer measurements than standard compressive sensing. A …
recovering signals from much fewer measurements than standard compressive sensing. A …
Error bounds for compressed sensing algorithms with group sparsity: A unified approach
ME Ahsen, M Vidyasagar - Applied and Computational Harmonic Analysis, 2017 - Elsevier
In compressed sensing, in order to recover a sparse or nearly sparse vector from possibly
noisy measurements, the most popular approach is ℓ 1-norm minimization. Upper bounds …
noisy measurements, the most popular approach is ℓ 1-norm minimization. Upper bounds …
Combinatorial algorithms for compressed sensing
G Cormode, S Muthukrishnan - … , SIROCCO 2006, Chester, UK, July 2-5 …, 2006 - Springer
In sparse approximation theory, the fundamental problem is to reconstruct a signal A∈ ℝ n
from linear measurements< A ψ i> with respect to a dictionary of ψ i's. Recently, there is …
from linear measurements< A ψ i> with respect to a dictionary of ψ i's. Recently, there is …
Analysis and algorithms for some compressed sensing models based on L1/L2 minimization
Recently, in a series of papers Y. Rahimi, C. Wang, H. Dong, and Y. Lou, SIAM J. Sci.
Comput., 41 (2019), pp. A3649--A3672; C. Wang, M. Tao, J. Nagy, and Y. Lou, SIAM J …
Comput., 41 (2019), pp. A3649--A3672; C. Wang, M. Tao, J. Nagy, and Y. Lou, SIAM J …
Tight performance bounds for compressed sensing with conventional and group sparsity
S Ranjan, M Vidyasagar - IEEE Transactions on Signal …, 2019 - ieeexplore.ieee.org
In this paper, we study the problem of recovering a group sparse vector from a small number
of linear measurements. In the past, the common approach has been to use various “group …
of linear measurements. In the past, the common approach has been to use various “group …
Message-passing algorithms for compressed sensing
Compressed sensing aims to undersample certain high-dimensional signals yet accurately
reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible …
reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible …
A tight bound of hard thresholding
J Shen, P Li - Journal of Machine Learning Research, 2018 - jmlr.org
This paper is concerned with the hard thresholding operator which sets all but the k largest
absolute elements of a vector to zero. We establish a tight bound to quantitatively …
absolute elements of a vector to zero. We establish a tight bound to quantitatively …
On low rank matrix approximations with applications to synthesis problem in compressed sensing
We consider the synthesis problem of compressed sensing—given s and an M× n matrix A,
extract from A an m× n submatrix A m, with m as small as possible, which is s-good, that is …
extract from A an m× n submatrix A m, with m as small as possible, which is s-good, that is …