FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2071607622> ?p ?o ?g. }

• W2071607622 endingPage "1433" @default.
• W2071607622 startingPage "1419" @default.
• W2071607622 abstract "This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery." @default.
• W2071607622 created "2016-06-24" @default.
• W2071607622 creator A5000852147 @default.
• W2071607622 creator A5091103447 @default.
• W2071607622 date "2013-03-01" @default.
• W2071607622 modified "2023-09-29" @default.
• W2071607622 title "Sparse Distributed Learning Based on Diffusion Adaptation" @default.
• W2071607622 cites W1595020299 @default.
• W2071607622 cites W1970997001 @default.
• W2071607622 cites W1976709621 @default.
• W2071607622 cites W1986302543 @default.
• W2071607622 cites W2018980815 @default.
• W2071607622 cites W2029080014 @default.
• W2071607622 cites W2053745833 @default.
• W2071607622 cites W2062365357 @default.
• W2071607622 cites W2076428552 @default.
• W2071607622 cites W2099282834 @default.
• W2071607622 cites W2099464104 @default.
• W2071607622 cites W2104266187 @default.
• W2071607622 cites W2107861471 @default.
• W2071607622 cites W2108924122 @default.
• W2071607622 cites W2109136671 @default.
• W2071607622 cites W2110482969 @default.
• W2071607622 cites W2111395440 @default.
• W2071607622 cites W2111714602 @default.
• W2071607622 cites W2115671189 @default.
• W2071607622 cites W2116437043 @default.
• W2071607622 cites W2118776392 @default.
• W2071607622 cites W2121820607 @default.
• W2071607622 cites W2130359405 @default.
• W2071607622 cites W2130442323 @default.
• W2071607622 cites W2136508948 @default.
• W2071607622 cites W2141788746 @default.
• W2071607622 cites W2145672718 @default.
• W2071607622 cites W2147108768 @default.
• W2071607622 cites W2151565294 @default.
• W2071607622 cites W2153966940 @default.
• W2071607622 cites W2155534503 @default.
• W2071607622 cites W2158491950 @default.
• W2071607622 cites W2163576063 @default.
• W2071607622 cites W2164452299 @default.
• W2071607622 cites W2169818402 @default.
• W2071607622 cites W2171529483 @default.
• W2071607622 cites W2401610261 @default.
• W2071607622 cites W2545418710 @default.
• W2071607622 cites W3139847728 @default.
• W2071607622 cites W4239240501 @default.
• W2071607622 cites W4240867252 @default.
• W2071607622 cites W4250955649 @default.
• W2071607622 cites W4302033506 @default.
• W2071607622 doi "https://doi.org/10.1109/tsp.2012.2232663" @default.
• W2071607622 hasPublicationYear "2013" @default.
• W2071607622 type Work @default.
• W2071607622 sameAs 2071607622 @default.
• W2071607622 citedByCount "196" @default.
• W2071607622 countsByYear W20716076222013 @default.
• W2071607622 countsByYear W20716076222014 @default.
• W2071607622 countsByYear W20716076222015 @default.
• W2071607622 countsByYear W20716076222016 @default.
• W2071607622 countsByYear W20716076222017 @default.
• W2071607622 countsByYear W20716076222018 @default.
• W2071607622 countsByYear W20716076222019 @default.
• W2071607622 countsByYear W20716076222020 @default.
• W2071607622 countsByYear W20716076222021 @default.
• W2071607622 countsByYear W20716076222022 @default.
• W2071607622 countsByYear W20716076222023 @default.
• W2071607622 crossrefType "journal-article" @default.
• W2071607622 hasAuthorship W2071607622A5000852147 @default.
• W2071607622 hasAuthorship W2071607622A5091103447 @default.
• W2071607622 hasBestOaLocation W20716076222 @default.
• W2071607622 hasConcept C111110010 @default.
• W2071607622 hasConcept C112680207 @default.
• W2071607622 hasConcept C11413529 @default.
• W2071607622 hasConcept C119857082 @default.
• W2071607622 hasConcept C121332964 @default.
• W2071607622 hasConcept C124851039 @default.
• W2071607622 hasConcept C126255220 @default.
• W2071607622 hasConcept C154945302 @default.
• W2071607622 hasConcept C157972887 @default.
• W2071607622 hasConcept C162324750 @default.
• W2071607622 hasConcept C163716315 @default.
• W2071607622 hasConcept C165696696 @default.
• W2071607622 hasConcept C2524010 @default.
• W2071607622 hasConcept C2776135515 @default.
• W2071607622 hasConcept C2777303404 @default.
• W2071607622 hasConcept C3017618536 @default.
• W2071607622 hasConcept C33923547 @default.
• W2071607622 hasConcept C38652104 @default.
• W2071607622 hasConcept C41008148 @default.
• W2071607622 hasConcept C50522688 @default.
• W2071607622 hasConcept C56372850 @default.
• W2071607622 hasConcept C56739046 @default.
• W2071607622 hasConcept C62520636 @default.
• W2071607622 hasConcept C68710425 @default.
• W2071607622 hasConceptScore W2071607622C111110010 @default.
• W2071607622 hasConceptScore W2071607622C112680207 @default.
• W2071607622 hasConceptScore W2071607622C11413529 @default.
• W2071607622 hasConceptScore W2071607622C119857082 @default.

• next