Matches in SemOpenAlex for { <https://semopenalex.org/work/W2091710249> ?p ?o ?g. }
Showing items 1 to 60 of
60
with 100 items per page.
- W2091710249 abstract "We use the approximate message passing framework (AMP) [1] to address the problem of recovering a sparse vector from undersampled noisy measurements. We propose an algorithm based on Sparse Bayesian learning (SBL) [2]. Unlike the original EM based SBL that requires matrix inversions, the proposed algorithm has linear complexity, which makes it well suited for large scale problems. Compared to other message passing techniques, the algorithm requires fewer approximations, due to the conditional Gaussian prior assumption on the original vector. Numerical results show that the proposed algorithm has comparable and in many cases better performance than existing algorithms despite significant reduction in complexity." @default.
- W2091710249 created "2016-06-24" @default.
- W2091710249 creator A5001700017 @default.
- W2091710249 creator A5070524265 @default.
- W2091710249 date "2014-11-01" @default.
- W2091710249 modified "2023-10-18" @default.
- W2091710249 title "Sparse Bayesian learning using approximate message passing" @default.
- W2091710249 cites W2018731202 @default.
- W2091710249 cites W2020282020 @default.
- W2091710249 cites W2082029531 @default.
- W2091710249 cites W2122315118 @default.
- W2091710249 cites W2146000945 @default.
- W2091710249 cites W2148154358 @default.
- W2091710249 cites W2152279006 @default.
- W2091710249 cites W2154153158 @default.
- W2091710249 doi "https://doi.org/10.1109/acssc.2014.7094812" @default.
- W2091710249 hasPublicationYear "2014" @default.
- W2091710249 type Work @default.
- W2091710249 sameAs 2091710249 @default.
- W2091710249 citedByCount "26" @default.
- W2091710249 countsByYear W20917102492016 @default.
- W2091710249 countsByYear W20917102492017 @default.
- W2091710249 countsByYear W20917102492018 @default.
- W2091710249 countsByYear W20917102492019 @default.
- W2091710249 countsByYear W20917102492020 @default.
- W2091710249 countsByYear W20917102492021 @default.
- W2091710249 countsByYear W20917102492022 @default.
- W2091710249 countsByYear W20917102492023 @default.
- W2091710249 crossrefType "proceedings-article" @default.
- W2091710249 hasAuthorship W2091710249A5001700017 @default.
- W2091710249 hasAuthorship W2091710249A5070524265 @default.
- W2091710249 hasConcept C107673813 @default.
- W2091710249 hasConcept C11413529 @default.
- W2091710249 hasConcept C154945302 @default.
- W2091710249 hasConcept C173608175 @default.
- W2091710249 hasConcept C41008148 @default.
- W2091710249 hasConcept C854659 @default.
- W2091710249 hasConceptScore W2091710249C107673813 @default.
- W2091710249 hasConceptScore W2091710249C11413529 @default.
- W2091710249 hasConceptScore W2091710249C154945302 @default.
- W2091710249 hasConceptScore W2091710249C173608175 @default.
- W2091710249 hasConceptScore W2091710249C41008148 @default.
- W2091710249 hasConceptScore W2091710249C854659 @default.
- W2091710249 hasLocation W20917102491 @default.
- W2091710249 hasOpenAccess W2091710249 @default.
- W2091710249 hasPrimaryLocation W20917102491 @default.
- W2091710249 hasRelatedWork W1495535967 @default.
- W2091710249 hasRelatedWork W1509819746 @default.
- W2091710249 hasRelatedWork W1554606847 @default.
- W2091710249 hasRelatedWork W1834449470 @default.
- W2091710249 hasRelatedWork W2098337892 @default.
- W2091710249 hasRelatedWork W2165172827 @default.
- W2091710249 hasRelatedWork W2281761054 @default.
- W2091710249 hasRelatedWork W2368564840 @default.
- W2091710249 hasRelatedWork W2392091800 @default.
- W2091710249 hasRelatedWork W2610473248 @default.
- W2091710249 isParatext "false" @default.
- W2091710249 isRetracted "false" @default.
- W2091710249 magId "2091710249" @default.
- W2091710249 workType "article" @default.