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Distributed Greedy Sparse Learning over Doubly Stochastic Networks
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
2017 (English)In: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2017, p. 361-364Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE , 2017. p. 361-364
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-226275ISI: 000426986000073Scopus ID: 2-s2.0-85041494941ISBN: 978-0-9928-6267-1 OAI: oai:DiVA.org:kth-226275DiVA, id: diva2:1199508
Conference
25th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2017, GREECE
Note

QC 20180420

Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-04-20Bibliographically approved

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Chatterjee, SaikatRasmussen, Lars Kildehöj

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