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Greedy Sparse Learning Over Network
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
2018 (English)In: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, ISSN 2373-776X, Vol. 4, no 3, p. 424-435Article in journal (Refereed) Published
Abstract [en]

In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted version of their individual intermediate estimates over the network. We provide a restricted-isometry-property (RIP)-based theoretical performance guarantee in the presence of additive noise. In the absence of noise, we show that under certain conditions on the RIP-constant of measurement matrix at each node of the network, the individual node estimates collectively converge to the true sparse signal. Furthermore, we provide an upper bound on the number of iterations required by the greedy algorithm to converge. Through simulations, we also show that the practical performance of the proposed algorithm is better than other state-of-the-art distributed greedy algorithms found in the literature.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 4, no 3, p. 424-435
Keywords [en]
Adaptive estimation, compressed sensing, convergence, distributed algorithms, greedy algorithms
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-235601DOI: 10.1109/TSIPN.2017.2710905ISI: 000444834500001Scopus ID: 2-s2.0-85044864986OAI: oai:DiVA.org:kth-235601DiVA, id: diva2:1252160
Note

QC 20181001

Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved

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Zaki, AhmedVenkitaraman, ArunChatterjee, SaikatRasmussen, Lars Kildehoj
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Output format
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