{"id":"https://openalex.org/W3100420365","doi":"https://doi.org/10.1109/tip.2016.2556582","title":"Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing","display_name":"Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing","publication_year":2016,"publication_date":"2016-04-20","ids":{"openalex":"https://openalex.org/W3100420365","doi":"https://doi.org/10.1109/tip.2016.2556582","mag":"3100420365","pmid":"https://pubmed.ncbi.nlm.nih.gov/28113900"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2016.2556582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2016.2556582","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1505.06270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067626381","display_name":"Jun Fang","orcid":"https://orcid.org/0000-0001-7427-4723"},"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":true,"raw_author_name":"Jun Fang","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-7427-4723","affiliations":[{"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/A5048488443","display_name":"Lizao Zhang","orcid":null},"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":"Lizao Zhang","raw_affiliation_strings":["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":"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/A5100365817","display_name":"Hongbin Li","orcid":"https://orcid.org/0000-0003-1453-847X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongbin Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067626381"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":8.3331,"has_fulltext":false,"cited_by_count":100,"citation_normalized_percentile":{"value":0.98219963,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"25","issue":"6","first_page":"2920","last_page":"2930"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7455042600631714},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.575150191783905},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5320998430252075},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5093616247177124},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5053063035011292},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4888541102409363},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.4878080189228058},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4722002446651459},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4566461443901062},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.45018067955970764},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44215941429138184},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.42960184812545776},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.399158239364624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3139704465866089},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09263280034065247}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7455042600631714},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.575150191783905},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5320998430252075},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5093616247177124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5053063035011292},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4888541102409363},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.4878080189228058},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4722002446651459},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4566461443901062},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.45018067955970764},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44215941429138184},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42960184812545776},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.399158239364624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3139704465866089},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09263280034065247},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2016.2556582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2016.2556582","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:28113900","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28113900","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 image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:1505.06270","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1505.06270","pdf_url":"https://arxiv.org/pdf/1505.06270","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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:1505.06270","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1505.06270","pdf_url":"https://arxiv.org/pdf/1505.06270","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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/G1437821091","display_name":null,"funder_award_id":"U1530154","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2212680630","display_name":null,"funder_award_id":"ECCS-1408182","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3596787953","display_name":null,"funder_award_id":"61522104","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4391136534","display_name":null,"funder_award_id":"61428103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"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":32,"referenced_works":["https://openalex.org/W1608074891","https://openalex.org/W1981157266","https://openalex.org/W1986931325","https://openalex.org/W2026933032","https://openalex.org/W2028823365","https://openalex.org/W2033419225","https://openalex.org/W2047499058","https://openalex.org/W2049502219","https://openalex.org/W2071282831","https://openalex.org/W2071284784","https://openalex.org/W2083042020","https://openalex.org/W2100526560","https://openalex.org/W2101382837","https://openalex.org/W2108445923","https://openalex.org/W2125680629","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2135780853","https://openalex.org/W2138019504","https://openalex.org/W2146000945","https://openalex.org/W2147276092","https://openalex.org/W2152279006","https://openalex.org/W2161765392","https://openalex.org/W2166670884","https://openalex.org/W2543631487","https://openalex.org/W2553907426","https://openalex.org/W2762625016","https://openalex.org/W2894923989","https://openalex.org/W2963206527","https://openalex.org/W3125735862","https://openalex.org/W4234595371","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4309133645","https://openalex.org/W2012910518","https://openalex.org/W2116723448","https://openalex.org/W2070699059","https://openalex.org/W2137666346","https://openalex.org/W4244110343","https://openalex.org/W4389911004","https://openalex.org/W2067883763","https://openalex.org/W2108678517"],"abstract_inverted_index":{"We":[0,94],"consider":[1],"the":[2,35,76,88,104,111,119,139,148],"problem":[3],"of":[4,75,87,91,129],"recovering":[5],"2D":[6,14,42,130],"block-sparse":[7,15,37],"signals":[8],"with":[9,110],"unknown":[10],"cluster":[11,79],"patterns.":[12],"The":[13,48],"patterns":[16],"arise":[17],"naturally":[18],"in":[19,147],"many":[20],"practical":[21],"applications,":[22],"such":[23],"as":[24],"foreground":[25],"detection":[26],"and":[27,72,134],"inverse":[28],"synthetic":[29],"aperture":[30],"radar":[31],"imaging.":[32],"To":[33],"exploit":[34],"underlying":[36,77],"structure,":[38],"we":[39],"propose":[40],"a":[41,56,84,96,127,144],"pattern-coupled":[43,50],"hierarchical":[44,51],"Gaussian":[45,52],"prior":[46,53,113],"model.":[47,114],"proposed":[49,112,120],"model":[54],"imposes":[55],"soft":[57],"coupling":[58,68],"mechanism":[59,69],"among":[60],"neighboring":[61],"coefficients":[62],"through":[63],"their":[64],"shared":[65],"hyperparameters.":[66],"This":[67],"enables":[70],"effective":[71],"automatic":[73],"learning":[74],"irregular":[78],"patterns,":[80],"without":[81],"requiring":[82],"any":[83],"priori":[85],"knowledge":[86],"block":[89],"partition":[90],"sparse":[92,131],"signals.":[93],"develop":[95],"computationally":[97],"efficient":[98],"Bayesian":[99],"inference":[100],"method,":[101,141],"which":[102],"integrates":[103],"generalized":[105],"approximate":[106],"message":[107],"passing":[108],"technique":[109],"Simulation":[115],"results":[116],"show":[117],"that":[118],"method":[121],"offers":[122],"competitive":[123],"recovery":[124,133],"performance":[125],"for":[126],"range":[128],"signal":[132],"image":[135],"processing":[136],"applications":[137],"over":[138],"existing":[140],"meanwhile":[142],"achieving":[143],"significant":[145],"reduction":[146],"computational":[149],"complexity.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
