Matches in SemOpenAlex for { <https://semopenalex.org/work/W2920485214> ?p ?o ?g. }
- W2920485214 endingPage "247" @default.
- W2920485214 startingPage "247" @default.
- W2920485214 abstract "We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the amount of clumpiness in the supports of the solution to improve the recovery performance of sparse signals with an unknown clustering pattern. This parameter does not exist in the other existing algorithms and is learned via our hierarchical SBL algorithm. While the proposed algorithm is constructed for the MMVs, it can also be applied to the single measurement vector (SMV) problems. Simulation results show the effectiveness of our algorithm compared to other algorithms for both SMV and MMVs." @default.
- W2920485214 created "2019-03-11" @default.
- W2920485214 creator A5015356150 @default.
- W2920485214 creator A5041641170 @default.
- W2920485214 creator A5066135276 @default.
- W2920485214 date "2019-03-05" @default.
- W2920485214 modified "2023-09-26" @default.
- W2920485214 title "Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns" @default.
- W2920485214 cites W1857867064 @default.
- W2920485214 cites W1893876825 @default.
- W2920485214 cites W1980300788 @default.
- W2920485214 cites W1981157266 @default.
- W2920485214 cites W2020282020 @default.
- W2920485214 cites W2025797389 @default.
- W2920485214 cites W2026933032 @default.
- W2920485214 cites W2033419225 @default.
- W2920485214 cites W2046612908 @default.
- W2920485214 cites W2060668003 @default.
- W2920485214 cites W2071282831 @default.
- W2920485214 cites W2071284784 @default.
- W2920485214 cites W2100556411 @default.
- W2920485214 cites W2100864539 @default.
- W2920485214 cites W2104266187 @default.
- W2920485214 cites W2112796928 @default.
- W2920485214 cites W2114129195 @default.
- W2920485214 cites W2119667497 @default.
- W2920485214 cites W2123629701 @default.
- W2920485214 cites W2135780853 @default.
- W2920485214 cites W2135859872 @default.
- W2920485214 cites W2138019504 @default.
- W2920485214 cites W2140514146 @default.
- W2920485214 cites W2141159272 @default.
- W2920485214 cites W2145096794 @default.
- W2920485214 cites W2146000945 @default.
- W2920485214 cites W2148154358 @default.
- W2920485214 cites W2148534890 @default.
- W2920485214 cites W2152279006 @default.
- W2920485214 cites W2153609494 @default.
- W2920485214 cites W2162409952 @default.
- W2920485214 cites W2164696938 @default.
- W2920485214 cites W2168123400 @default.
- W2920485214 cites W2290775341 @default.
- W2920485214 cites W2292697821 @default.
- W2920485214 cites W2511885285 @default.
- W2920485214 cites W2549995704 @default.
- W2920485214 cites W2563113662 @default.
- W2920485214 cites W3099013461 @default.
- W2920485214 cites W3100420365 @default.
- W2920485214 cites W3124114587 @default.
- W2920485214 cites W4250955649 @default.
- W2920485214 doi "https://doi.org/10.3390/e21030247" @default.
- W2920485214 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7514728" @default.
- W2920485214 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33266961" @default.
- W2920485214 hasPublicationYear "2019" @default.
- W2920485214 type Work @default.
- W2920485214 sameAs 2920485214 @default.
- W2920485214 citedByCount "18" @default.
- W2920485214 countsByYear W29204852142019 @default.
- W2920485214 countsByYear W29204852142020 @default.
- W2920485214 countsByYear W29204852142021 @default.
- W2920485214 countsByYear W29204852142022 @default.
- W2920485214 countsByYear W29204852142023 @default.
- W2920485214 crossrefType "journal-article" @default.
- W2920485214 hasAuthorship W2920485214A5015356150 @default.
- W2920485214 hasAuthorship W2920485214A5041641170 @default.
- W2920485214 hasAuthorship W2920485214A5066135276 @default.
- W2920485214 hasBestOaLocation W29204852141 @default.
- W2920485214 hasConcept C106487976 @default.
- W2920485214 hasConcept C107673813 @default.
- W2920485214 hasConcept C11413529 @default.
- W2920485214 hasConcept C121332964 @default.
- W2920485214 hasConcept C124101348 @default.
- W2920485214 hasConcept C124851039 @default.
- W2920485214 hasConcept C151730666 @default.
- W2920485214 hasConcept C153180895 @default.
- W2920485214 hasConcept C154945302 @default.
- W2920485214 hasConcept C159985019 @default.
- W2920485214 hasConcept C163716315 @default.
- W2920485214 hasConcept C192562407 @default.
- W2920485214 hasConcept C23123220 @default.
- W2920485214 hasConcept C2779343474 @default.
- W2920485214 hasConcept C2780009758 @default.
- W2920485214 hasConcept C41008148 @default.
- W2920485214 hasConcept C56372850 @default.
- W2920485214 hasConcept C62520636 @default.
- W2920485214 hasConcept C73555534 @default.
- W2920485214 hasConcept C86803240 @default.
- W2920485214 hasConcept C88548561 @default.
- W2920485214 hasConcept C92835128 @default.
- W2920485214 hasConceptScore W2920485214C106487976 @default.
- W2920485214 hasConceptScore W2920485214C107673813 @default.
- W2920485214 hasConceptScore W2920485214C11413529 @default.
- W2920485214 hasConceptScore W2920485214C121332964 @default.
- W2920485214 hasConceptScore W2920485214C124101348 @default.
- W2920485214 hasConceptScore W2920485214C124851039 @default.
- W2920485214 hasConceptScore W2920485214C151730666 @default.
- W2920485214 hasConceptScore W2920485214C153180895 @default.
- W2920485214 hasConceptScore W2920485214C154945302 @default.