Results 31 to 40 of about 31,241 (250)
Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing [PDF]
, 2015 This paper studies the convergence of the adaptively iterative thresholding (AIT) algorithm for compressed sensing. We first introduce a generalized restricted isometry property (gRIP).
Chang, Xiangyu +4 more
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Perturbations of Compressed Data Separation With Redundant Tight Frames
IEEE Access, 2018 In the era of big data, the multi-modal data can be seen everywhere. Research on such data has attracted extensive attention in the past few years. In this paper, we investigate the perturbations of compressed data separation with redundant tight frames ...
Feng Zhang +4 more
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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization [PDF]
, 2007 The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and ...
Benjamin Recht +4 more
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On the Certification of the Restricted Isometry Property
CoRR, 2011 Compressed sensing is a technique for finding sparse solutions to underdetermined linear systems. This technique relies on properties of the sensing matrix such as the restricted isometry property. Sensing matrices that satisfy the restricted isometry property with optimal parameters are mainly obtained via probabilistic arguments.
Pascal Koiran, Anastasios Zouzias
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On Recovery of Block Sparse Signals via Block Compressive Sampling Matching Pursuit
IEEE Access, 2019 Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been ...
Xiaobo Zhang +4 more
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Restricted p-Isometry Properties of Partially Sparse Signal Recovery
Discrete Dynamics in Nature and Society, 2013 By generalizing the restricted p-isometry property to the partially sparse signal recovery problem, we give a sufficient condition for exactly recovering partially sparse signal via the partial lp minimization (truncated lp minimization) problem with p ...
Haini Bi, Lingchen Kong, Naihua Xiu
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A New Nonconvex Sparse Recovery Method for Compressive Sensing
Frontiers in Applied Mathematics and Statistics, 2019 As an extension of the widely used ℓr-minimization with 0 < r ≤ 1, a new non-convex weighted ℓr − ℓ1 minimization method is proposed for compressive sensing.
Zhiyong Zhou, Jun Yu
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A Sharp RIP Condition for Orthogonal Matching Pursuit
Abstract and Applied Analysis, 2013 A restricted isometry property (RIP) condition δK+KθK ...
Wei Dan
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Efficiency of Orthogonal Matching Pursuit for Group Sparse Recovery
Axioms, 2023 We propose the Group Orthogonal Matching Pursuit (GOMP) algorithm to recover group sparse signals from noisy measurements. Under the group restricted isometry property (GRIP), we prove the instance optimality of the GOMP algorithm for any decomposable ...
Chunfang Shao +3 more
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The Average-Case Time Complexity of Certifying the Restricted Isometry Property [PDF]
IEEE Transactions on Information Theory, 2021 In compressed sensing, the restricted isometry property (RIP) on $M \times N$ sensing matrices (where $M < N$) guarantees efficient reconstruction of sparse vectors. A matrix has the $(s,δ)$-$\mathsf{RIP}$ property if behaves as a $δ$-approximate isometry on $s$-sparse vectors. It is well known that an $M\times N$ matrix with i.i.d. $\mathcal{N}(0,1/
Yunzi Ding +3 more
openaire +2 more sources