Greedy Sparse Learning Over Network

Zaki, Ahmed; Venkitaraman, Arun; Chatterjee, Saikat; Rasmussen, Lars K.

doi:10.1109/tsipn.2017.2710905

Cited by 12 publications

(8 citation statements)

References 39 publications

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“…The pruned BPDN (pNBPDN) is more close to distributed greedy algorithms that work with the same system setup. Examples of the greedy algorithms are network greedy pursuit (NGP) [22] and distributed hard thresholding pursuit (DHTP) [31]. We mention that NGP and DHTP have RIP conditions δ 3s (A l ) < 0.362 and δ 3s (A l ) < 0.333, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Based on subspace pursuit [19] and CoSamp [20] algorithms used for centralized sparse learning, a set of distributed algorithms are proposed in [14], [21] that provide a high computational advantage. With the same motivation of low computational complexity, a recent work on designing distributed greedy pursuit algorithms is [22].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Our algorithms are locally optimum at each node of the network. This endeavor is an extension of past work in designing distributed greedy algorithms [31], [22] to the regime of convex optimization.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Convex Optimization Based Sparse Learning Over Networks

Zaki

Chatterjee

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the network and reduce processing time. Each node of the network uses a convex optimization based algorithm that provides a locally optimum solution for that node. The nodes exchange their signal estimates over the network in order to refine their local estimates. At a node, the optimization algorithm is based on an ℓ1-norm minimization with appropriate modifications to promote sparsity as well as to include influence of estimates from neighboring nodes. Our expectation is that local estimates in each node improve fast and converge, resulting in a limited demand for communication of estimates between nodes and reducing the processing time. We provide restricted-isometry-property (RIP)-based theoretical analysis on estimation quality. In the scenario of clean observation, it is shown that the local estimates converge to the exact sparse signal under certain technical conditions. Simulation results show that the proposed algorithms show competitive performance compared to a globally optimum distributed LASSO algorithm in the sense of convergence speed and estimation error.

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Section: Discussionmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

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Convex Optimization Based Sparse Learning Over Networks

Zaki

Chatterjee

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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“…Therefore we focus on developing distributed greedy sparse learning algorithms that exchange intermediate estimates of x or relevant parameters over network. Relevant past works in this direction are [11], [12], [13] where we proposed some rules for information exchange over network and developed new greedy algorithms for various signal models. In this current article, we investigate an existing distributed greedy algorithm in the literature [14].…”

Section: Introductionmentioning

confidence: 99%

Estimate exchange over network is good for distributed hard thresholding pursuit

et al. 2019

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We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhead for information transmission. Our objective in this article is to show that the strategy is good for learning in spite of limited communication. In pursuit of this objective, we first provide a restricted isometry property (RIP)-based theoretical analysis on convergence of the iterative algorithm. Then, using simulations, we show that the algorithm provides competitive performance in learning sparse signals vis-a-vis an existing alternate distributed algorithm. The alternate distributed algorithm exchanges more information including observations and system parameters.

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“…However, their algorithm relies on the knowledge of the data of each sensor, thus the performance decays when the number of sensor increases. In [25] a distributed greedy algorithm for sparse learning is proposed, the algorithm is not based in a consensus scheme but instead it is designed to achieve efficiency through cooperation and information exchange. In [6] a framework for distributed minimization of non-convex functions is proposed, such framework is based on differences of gradients using successive convex approximations and the use of consensus to distribute the computation among network nodes, each node solves a local convex approximation problem following by local averaging operations.…”

Section: Introductionmentioning

confidence: 99%

Compress sensing algorithm for estimation of signals in sensor networks

et al. 2019

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In this research, we present a data recovery scheme for wireless sensor networks. In some sensor networks, each node must be able to recover the complete information of the network, which leads to the problem of the high cost of energy in communication and storage of information. We proposed a modified gossip algorithm for acquire distributed measurements and communicate the information across all nodes of the network using compressive sampling and Gossip algorithms to compact the data to be stored and transmitted through a network. The experimental results on synthetic data show that the proposed method reconstruct better the signal and in less iterations than with a similar method using a thresholding algorithm.

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Greedy Sparse Learning Over Network

Cited by 12 publications

References 39 publications

Convex Optimization Based Sparse Learning Over Networks

Convex Optimization Based Sparse Learning Over Networks

Estimate exchange over network is good for distributed hard thresholding pursuit

Compress sensing algorithm for estimation of signals in sensor networks

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