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Stochastic Greedy Algorithms For Multiple Measurement Vectors
TL;DR: In this article, the authors developed stochastic greedy algorithms for solving the joint sparse MMV reconstruction problem, where the underlying signal is assumed to have joint sparse structures, and they also utilized the mini-batching technique to further improve their performance.
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Abstract: Sparse representation of a single measurement vector (SMV) has been explored in a variety of compressive sensing applications. Recently, SMV models have been extended to solve multiple measurement vectors (MMV) problems, where the underlying signal is assumed to have joint sparse structures. To circumvent the NP-hardness of the $\ell_0$ minimization problem, many deterministic MMV algorithms solve the convex relaxed models with limited efficiency. In this paper, we develop stochastic greedy algorithms for solving the joint sparse MMV reconstruction problem. In particular, we propose the MMV Stochastic Iterative Hard Thresholding (MStoIHT) and MMV Stochastic Gradient Matching Pursuit (MStoGradMP) algorithms, and we also utilize the mini-batching technique to further improve their performance. Convergence analysis indicates that the proposed algorithms are able to converge faster than their SMV counterparts, i.e., concatenated StoIHT and StoGradMP, under certain conditions. Numerical experiments have illustrated the superior effectiveness of the proposed algorithms over their SMV counterparts.
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Citations
Simultaneous Blind Deconvolution and Phase Retrieval with Tensor Iterative Hard Thresholding
Shuang Li,Gongguo Tang,Michael B. Wakin +2 more
- 12 May 2019
TL;DR: This work shows that this non-linear problem can be reformulated as a low-rank tensor recovery problem and proposes an algorithm named TIHT-BDPR to recover the unknown parameters.
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Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery
Rachel Grotheer,Shuang Li,Anna Ma,Deanna Needell,Jing Qin +4 more
- 02 Feb 2020
TL;DR: This work aims to extend the stochastic iterative hard thresholding algorithm from vectors to tensors in order to address the problem of recovering a low-Tucker-rank tensor from its linear measurements.
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SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
TL;DR: SiSiBBlInGS as mentioned in this paper employs a graph-based dictionary learning approach for BB discovery, simultaneously considers both inter-and intra-state relationships in the data, can extract non-orthogonal components, and allows for variations in session counts and duration across states.
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Federated Gradient Matching Pursuit
TL;DR: In this paper , a federated gradient matching pursuit (FedGradMP) algorithm is proposed to solve the sparsity constrained minimization problem in the federated learning (FL) setting.
New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted $\ell_{2,1}$ Minimization
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TL;DR: Improved sufficient conditions based on the restricted isometry property (RIP) are shown for the exact and stable recovery of the recovery of $ {X}$ via the standard mixed-norm minimization.
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