Results 231 to 240 of about 66,822 (262)

On uniqueness of sparse signal recovery

Signal Processing, 2018
Abstract A basic issue of sparse signal recovery (SSR) is to explore the condition of the uniqueness with regard to the solution of the relevant optimization framework. However, the standard uniqueness conditions, such as spark condition, NSP (null space property), RIP (restricted isometry property) and mutual coherence condition, are with respect to
Xiao-Li Hu, Jiajun Wen 0001, Wai Keung Wong, Le Tong, Jinrong Cui   +4 more
openaire   +1 more source

Invariancy of Sparse Recovery Algorithms

IEEE Transactions on Information Theory, 2017
In this paper, a property for sparse recovery algorithms, called invariancy , is introduced. The significance of invariancy is that the performance of the algorithms with this property is less affected when the sensing (i.e., the dictionary) is ill-conditioned.
Milad Kharratzadeh, Arsalan Sharif-Nassab, Massoud Babaie-Zadeh   +2 more
openaire   +1 more source

Subspace penalized sparse learning for joint sparse recovery

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
The multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of the important contributions of this paper is to reveal that the seemingly least related state-of-art MMV joint sparse recovery algorithms - M-SBL (multiple sparse Bayesian ...
Jong Chul Ye, Jong Min Kim 0002, Yoram Bresler   +2 more
openaire   +1 more source

Bootstrapped sparse Bayesian learning for sparse signal recovery

2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
In this article we study the sparse signal recovery problem in a Bayesian framework using a novel Bootstrapped Sparse Bayesian Learning method. Sparse Bayesian Learning (SBL) framework is an effective tool for pruning out the irrelevant features and ending up with a sparse representation.
Ritwik Giri, Bhaskar D. Rao
openaire   +1 more source

Sparse recovery and Kronecker products

2010 44th Annual Conference on Information Sciences and Systems (CISS), 2010
In this note will consider sufficient conditions for sparse recovery such as Spark, coherence, restricted isometry property (RIP) and null space property (NSP). Then we will discuss the solution of underdetermined linear equations when the matrix is the Kronecker product of matrices. Specially we will explain how NSP behave in the case where the matrix
openaire   +1 more source

Sparse Recovery-Based Error Concealment

IEEE Transactions on Multimedia, 2017
no ...
Akbari, Ali, Trocan, Maria, Granado, Bertrand   +2 more
openaire   +2 more sources

Sparse Recovery with Brownian Sensing.

2011
We consider the problem of recovering the parameter α of a sparse function f (i.e. the number of non-zero entries of α is small compared to the number K of features) given noisy evaluations of f at a set of well-chosen sampling points. We introduce an additional randomization process, called Brownian sensing, based on the computation of stochastic ...
Carpentier, Alexandra, Maillard, Odalric, Munos, Rémi   +2 more
openaire   +2 more sources

Sparse Recovery

2020
Vladimir Shikhman, David Müller
openaire   +2 more sources

Jointly Sparse Signal Recovery and Support Recovery via Deep Learning With Applications in MIMO-Based Grant-Free Random Access

IEEE Journal on Selected Areas in Communications, 2021
Ying Cui
exaly  

Group Sparse Optimization for Images Recovery Using Capped Folded Concave Functions

SIAM Journal on Imaging Sciences, 2021
Xiaojun Chen
exaly