Design and Analysis of a Greedy Pursuit for Distributed Compressed Sensing

Sundman, Dennis; Chatterjee, Saikat; Skoglund, Mikael

doi:10.1109/tsp.2016.2523462

Cited by 30 publications

(28 citation statements)

References 59 publications

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“…Our objective is to achieve a lower DRE considering the whole decentralized network. We also adopt the average support-set cardinality error (ASCE) as a direct evaluation of the support-set recovery performance [12]. Note that the ASCE has the range

false[0, 1 false]

, and our objective is to achieve a lower ASCE.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…In the above algorithm, “

v o t e_{1}

” denotes the voting procedure [12]. Since an index present in two nodes’ candidate support sets will be treated as an element of the estimated common support set, the probability of the event that the estimated common support set contains the true common support set is very high.…”

Section: Distributed Compact Sensing Matrix Pursuit Algorithmmentioning

confidence: 99%

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An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network

Liu

Huang

Zhang

2017

Sensors

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We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP algorithm is capable of tackling the unknown sparsity problem successfully.

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false[0, 1 false]

, and our objective is to achieve a lower ASCE.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…In the above algorithm, “

v o t e_{1}

Section: Distributed Compact Sensing Matrix Pursuit Algorithmmentioning

confidence: 99%

An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network

Liu

Huang

Zhang

2017

Sensors

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show abstract

“…where (a) follows from Lemma 4 and (b) follows from (15). The first term in RHS of the above inequality can be bounded…”

Section: E Proof Of Theoremmentioning

confidence: 95%

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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“…In this context, low complexity distributed algorithms for large-scale sparse learning has a high potential [2], [3]. To realize large-scale distributed sparse learning, recent activities are reported in [4], [5] where greedy algorithms are designed and analyzed. A major advantage of greedy algorithms is their low computational complexity and hence their suitability for large-scale scenarios.…”

Section: Introductionmentioning

confidence: 99%

Distributed greedy sparse learning over doubly stochastic networks

Zaki

Venkitaraman

Chatterjee

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we develop a greedy algorithm for sparse learning over a doubly stochastic network. In the proposed algorithm, nodes of the network perform sparse learning by exchanging their individual intermediate variables. The algorithm is iterative in nature. We provide a restricted isometry property (RIP)-based theoretical guarantee both on the performance of the algorithm and the number of iterations required for convergence. Using simulations, we show that the proposed algorithm provides good performance.

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Design and Analysis of a Greedy Pursuit for Distributed Compressed Sensing

Cited by 30 publications

References 59 publications

An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network

An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network

Convex Optimization Based Sparse Learning Over Networks

Distributed greedy sparse learning over doubly stochastic networks

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