{"id":"https://openalex.org/W3014359050","doi":"https://doi.org/10.1109/tsp.2020.2983827","title":"Variance State Propagation for Structured Sparse Bayesian Learning","display_name":"Variance State Propagation for Structured Sparse Bayesian Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3014359050","doi":"https://doi.org/10.1109/tsp.2020.2983827","mag":"3014359050"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2020.2983827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.2983827","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.07352","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066980538","display_name":"Mingchen Zhang","orcid":"https://orcid.org/0000-0002-6846-8393"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingchen Zhang","raw_affiliation_strings":["Center for Intelligent Networking and Communications, Xiyuan, China","National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-6846-8393","affiliations":[{"raw_affiliation_string":"Center for Intelligent Networking and Communications, Xiyuan, China","institution_ids":[]},{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063946480","display_name":"Xiaojun Yuan","orcid":"https://orcid.org/0000-0002-0433-6535"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Yuan","raw_affiliation_strings":["Center for Intelligent Networking and Communications, Xiyuan, China","National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-0433-6535","affiliations":[{"raw_affiliation_string":"Center for Intelligent Networking and Communications, Xiyuan, China","institution_ids":[]},{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102895986","display_name":"Zhen-Qing He","orcid":"https://orcid.org/0000-0003-1408-2134"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen-Qing He","raw_affiliation_strings":["Center for Intelligent Networking and Communications, Xiyuan, China","National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Intelligent Networking and Communications, Xiyuan, China","institution_ids":[]},{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":1.5865,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.78960247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"68","issue":null,"first_page":"2386","last_page":"2400"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6505860686302185},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.638183057308197},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5912886261940002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5536202788352966},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5398508906364441},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5335548520088196},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5132379531860352},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.47844675183296204},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.47565075755119324},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41585463285446167},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.41278672218322754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3399057388305664},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2961624264717102},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21735909581184387}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6505860686302185},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.638183057308197},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5912886261940002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5536202788352966},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5398508906364441},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5335548520088196},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5132379531860352},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.47844675183296204},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.47565075755119324},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41585463285446167},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41278672218322754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3399057388305664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2961624264717102},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21735909581184387},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2020.2983827","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.2983827","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1910.07352","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.07352","pdf_url":"https://arxiv.org/pdf/1910.07352","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.07352","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.07352","pdf_url":"https://arxiv.org/pdf/1910.07352","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G585831099","display_name":null,"funder_award_id":"B20064","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G8382886","display_name":null,"funder_award_id":"61801084","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W422388727","https://openalex.org/W1774526428","https://openalex.org/W1981157266","https://openalex.org/W1997834106","https://openalex.org/W2000828982","https://openalex.org/W2018731202","https://openalex.org/W2026933032","https://openalex.org/W2029816571","https://openalex.org/W2033419225","https://openalex.org/W2056775112","https://openalex.org/W2071282831","https://openalex.org/W2078204800","https://openalex.org/W2082029531","https://openalex.org/W2100526560","https://openalex.org/W2112796928","https://openalex.org/W2119330592","https://openalex.org/W2127271355","https://openalex.org/W2131665779","https://openalex.org/W2135780853","https://openalex.org/W2137628444","https://openalex.org/W2138019504","https://openalex.org/W2147276092","https://openalex.org/W2164720308","https://openalex.org/W2166670884","https://openalex.org/W2244252827","https://openalex.org/W2289917018","https://openalex.org/W2520000847","https://openalex.org/W2538773003","https://openalex.org/W2553907426","https://openalex.org/W2604470139","https://openalex.org/W2605149632","https://openalex.org/W2777046509","https://openalex.org/W2933010605","https://openalex.org/W2945463326","https://openalex.org/W2963322354","https://openalex.org/W2963482940","https://openalex.org/W2963676935","https://openalex.org/W3005708632","https://openalex.org/W3048870145","https://openalex.org/W3100420365","https://openalex.org/W4232356881","https://openalex.org/W4285719527","https://openalex.org/W6677581936","https://openalex.org/W6728784960"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2141609920","https://openalex.org/W4294619368","https://openalex.org/W4286748465","https://openalex.org/W3118984993","https://openalex.org/W2144336328","https://openalex.org/W3196933554","https://openalex.org/W4380558509"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,66,153,162],"compressed":[3],"sensing":[4],"algorithm":[5,26,93,148],"termed":[6],"variance":[7],"state":[8,57],"propagation":[9],"(VSP)":[10],"for":[11],"block-sparse":[12,156],"signals,":[13],"i.e.,":[14],"sparse":[15,46],"signals":[16],"that":[17,119,145],"have":[18],"nonzero":[19],"coefficients":[20,79],"occurring":[21],"in":[22,44,100,111],"clusters.":[23],"The":[24,88,105,133],"VSP":[25,147],"is":[27,37,52,94,109,149],"developed":[28],"under":[29],"the":[30,41,45,56,59,62,70,98,101,112,115,139,146,166],"Bayesian":[31],"framework.":[32],"A":[33],"hierarchical":[34,67],"Gaussian":[35,63,103],"prior":[36,68,102],"introduced":[38,53],"to":[39,54,72,95,122,129,151],"depict":[40],"clustered":[42,74],"patterns":[43,75,81],"signal.":[47],"Markov":[48],"random":[49],"field":[50],"(MRF)":[51],"characterize":[55],"of":[58,61,91,114,155],"variances":[60,99],"priors.":[64],"Such":[65],"has":[69],"potential":[71],"encourage":[73],"and":[76,160],"suppress":[77],"isolated":[78],"whose":[80],"are":[82,120],"different":[83],"from":[84],"their":[85],"respective":[86],"neighbors.":[87],"core":[89],"idea":[90],"our":[92],"iteratively":[96],"update":[97],"distribution.":[104],"message":[106],"passing":[107],"technique":[108],"employed":[110],"design":[113,126],"algorithm.":[116],"For":[117],"messages":[118],"difficult":[121],"calculate,":[123],"we":[124],"correspondingly":[125],"reasonable":[127],"methods":[128],"achieve":[130],"approximate":[131],"calculations.":[132],"hyperparameters":[134],"can":[135],"be":[136],"updated":[137],"within":[138],"iteration":[140],"process.":[141],"Simulation":[142],"results":[143],"demonstrate":[144],"able":[150],"handle":[152],"variety":[154],"signal":[157],"recovery":[158],"tasks":[159],"presents":[161],"significant":[163],"advantage":[164],"over":[165],"existing":[167],"methods.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
