{"id":"https://openalex.org/W3122325486","doi":"https://doi.org/10.1109/tnnls.2020.3049056","title":"An Efficient Sparse Bayesian Learning Algorithm Based on Gaussian-Scale Mixtures","display_name":"An Efficient Sparse Bayesian Learning Algorithm Based on Gaussian-Scale Mixtures","publication_year":2021,"publication_date":"2021-01-22","ids":{"openalex":"https://openalex.org/W3122325486","doi":"https://doi.org/10.1109/tnnls.2020.3049056","mag":"3122325486","pmid":"https://pubmed.ncbi.nlm.nih.gov/33481719"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2020.3049056","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3049056","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075613088","display_name":"Wei Zhou","orcid":"https://orcid.org/0000-0001-5773-8012"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhou","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, China","State Key Laboratory of DigitalManufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-5773-8012","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]},{"raw_affiliation_string":"Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]},{"raw_affiliation_string":"State Key Laboratory of DigitalManufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hai-Tao Zhang","orcid":"https://orcid.org/0000-0002-8819-8829"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai-Tao Zhang","raw_affiliation_strings":["School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, China","State Key Laboratory of DigitalManufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8819-8829","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]},{"raw_affiliation_string":"Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]},{"raw_affiliation_string":"State Key Laboratory of DigitalManufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100384686","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-1305-5735"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["Department of Computer Science, School of Data Science, City University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-1305-5735","affiliations":[{"raw_affiliation_string":"Department of Computer Science, School of Data Science, City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.6936,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.98662752,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"33","issue":"7","first_page":"3065","last_page":"3078"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994999766349792,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/algorithm","display_name":"Algorithm","score":0.6239697933197021},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5659320950508118},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5439832210540771},{"id":"https://openalex.org/keywords/coordinate-descent","display_name":"Coordinate descent","score":0.5373275876045227},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4348887503147125},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4319304823875427},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.38282036781311035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32889413833618164}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6239697933197021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5659320950508118},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5439832210540771},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.5373275876045227},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4348887503147125},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4319304823875427},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.38282036781311035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32889413833618164},{"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/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/tnnls.2020.3049056","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3049056","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:33481719","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33481719","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6213055193","display_name":null,"funder_award_id":"61803168","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6816196766","display_name":null,"funder_award_id":"U1713203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6926619606","display_name":null,"funder_award_id":"61673330","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7308127663","display_name":null,"funder_award_id":"51721092","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W422388727","https://openalex.org/W866015105","https://openalex.org/W1648445109","https://openalex.org/W1969250928","https://openalex.org/W1977252496","https://openalex.org/W1985516812","https://openalex.org/W1988520084","https://openalex.org/W2004749383","https://openalex.org/W2038974670","https://openalex.org/W2048951385","https://openalex.org/W2065513175","https://openalex.org/W2071284784","https://openalex.org/W2074477564","https://openalex.org/W2105467943","https://openalex.org/W2124541940","https://openalex.org/W2127870457","https://openalex.org/W2145473366","https://openalex.org/W2146247053","https://openalex.org/W2146571341","https://openalex.org/W2148154358","https://openalex.org/W2149414429","https://openalex.org/W2161765392","https://openalex.org/W2286197641","https://openalex.org/W2422365436","https://openalex.org/W2474352580","https://openalex.org/W2539149443","https://openalex.org/W2596873572","https://openalex.org/W2790868002","https://openalex.org/W2892862852","https://openalex.org/W2908199533","https://openalex.org/W2911546748","https://openalex.org/W2926956015","https://openalex.org/W2942519784","https://openalex.org/W2942729349","https://openalex.org/W2963142478","https://openalex.org/W2972156166","https://openalex.org/W3012632571","https://openalex.org/W3087523553","https://openalex.org/W3100420365","https://openalex.org/W3100692477","https://openalex.org/W3106449828","https://openalex.org/W4212863985","https://openalex.org/W4241368925","https://openalex.org/W4242471985","https://openalex.org/W6636690510"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4318719684","https://openalex.org/W4389724018","https://openalex.org/W4318559728","https://openalex.org/W3183136280","https://openalex.org/W2775233965","https://openalex.org/W3114716045","https://openalex.org/W4360995913"],"abstract_inverted_index":{"Sparse":[0],"Bayesian":[1],"learning":[2,8],"(SBL)":[3],"is":[4,87,116,132],"a":[5,11,26,57,105,108,119,135,141],"popular":[6],"machine":[7],"approach":[9,172],"with":[10,36,112],"superior":[12],"generalization":[13],"capability":[14],"due":[15],"to":[16,90,100,163],"the":[17,67,92,145,165,170],"sparsity":[18],"of":[19,95,122,147,169,175],"its":[20,33],"adopted":[21],"model.":[22,61],"However,":[23],"it":[24],"entails":[25],"matrix":[27,102],"inversion":[28],"at":[29],"each":[30],"iteration,":[31],"hindering":[32],"practical":[34],"applications":[35],"large-scale":[37],"data":[38],"sets.":[39],"To":[40],"overcome":[41],"this":[42],"bottleneck,":[43],"we":[44],"propose":[45],"an":[46],"efficient":[47,69],"SBL":[48,70],"algorithm":[49,71],"with$\\mathcal":[50],"{O}(n^{2})$computational":[51],"complexity":[52],"per":[53],"iteration":[54],"based":[55],"on":[56,158],"Gaussian-scale":[58],"mixture":[59],"prior":[60],"By":[62],"specifying":[63],"two":[64,74],"different":[65,75],"hyperpriors,":[66],"proposed":[68,171],"can":[72],"meet":[73],"requirements,":[76],"such":[77],"as":[78],"high":[79,82],"efficiency":[80],"and":[81,98,125,154,167,178],"sparsity.":[83],"A":[84],"surrogate":[85],"function":[86,111],"introduced":[88],"herein":[89],"approximate":[91],"posterior":[93],"density":[94],"model":[96,123],"parameters":[97,124],"thereby":[99],"avoid":[101],"inversions.":[103],"Using":[104],"data-dependent":[106],"term,":[107],"joint":[109,120],"cost":[110],"separate":[113],"penalty":[114],"terms":[115,174],"reformulated":[117],"in":[118,140,173],"space":[121],"hyperparameters.":[126],"The":[127],"resulting":[128],"nonconvex":[129],"optimization":[130],"problem":[131],"solved":[133],"using":[134],"block":[136],"coordinate":[137],"descent":[138],"method":[139],"majorization\u2013minimization":[142],"framework.":[143],"Finally,":[144],"results":[146],"extensive":[148],"experiments":[149],"for":[150],"sparse":[151,155],"signal":[152],"recovery":[153],"image":[156],"reconstruction":[157],"benchmark":[159],"problems":[160],"are":[161],"elaborated":[162],"substantiate":[164],"effectiveness":[166],"superiority":[168],"computational":[176],"time":[177],"estimation":[179],"error.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
