{"id":"https://openalex.org/W3080594033","doi":"https://doi.org/10.1109/isit44484.2020.9174022","title":"A Novel B-MAP Proxy for Greedy Sparse Signal Recovery Algorithms","display_name":"A Novel B-MAP Proxy for Greedy Sparse Signal Recovery Algorithms","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3080594033","doi":"https://doi.org/10.1109/isit44484.2020.9174022","mag":"3080594033"},"language":"en","primary_location":{"id":"doi:10.1109/isit44484.2020.9174022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit44484.2020.9174022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5001728130","display_name":"Jeongmin Chae","orcid":"https://orcid.org/0000-0001-6007-1300"},"institutions":[{"id":"https://openalex.org/I57664883","display_name":"Ajou University","ror":"https://ror.org/03tzb2h73","country_code":"KR","type":"education","lineage":["https://openalex.org/I57664883"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jeongmin Chae","raw_affiliation_strings":["Ajou University,Electrical and Computer Engineering,Suwon,Korea","Electrical and Computer Engineering, Ajou University, Suwon, Korea"],"affiliations":[{"raw_affiliation_string":"Ajou University,Electrical and Computer Engineering,Suwon,Korea","institution_ids":["https://openalex.org/I57664883"]},{"raw_affiliation_string":"Electrical and Computer Engineering, Ajou University, Suwon, Korea","institution_ids":["https://openalex.org/I57664883"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084652371","display_name":"Song\u2010Nam Hong","orcid":"https://orcid.org/0000-0002-9535-2521"},"institutions":[{"id":"https://openalex.org/I57664883","display_name":"Ajou University","ror":"https://ror.org/03tzb2h73","country_code":"KR","type":"education","lineage":["https://openalex.org/I57664883"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Song-Nam Hong","raw_affiliation_strings":["Ajou University,Electrical and Computer Engineering,Suwon,Korea","Electrical and Computer Engineering, Ajou University, Suwon, Korea"],"affiliations":[{"raw_affiliation_string":"Ajou University,Electrical and Computer Engineering,Suwon,Korea","institution_ids":["https://openalex.org/I57664883"]},{"raw_affiliation_string":"Electrical and Computer Engineering, Ajou University, Suwon, Korea","institution_ids":["https://openalex.org/I57664883"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001728130"],"corresponding_institution_ids":["https://openalex.org/I57664883"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10010764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1409","last_page":"1413"},"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":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/matching-pursuit","display_name":"Matching pursuit","score":0.8769291639328003},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.6658346652984619},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6636056900024414},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6390891075134277},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.63592529296875},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5993738174438477},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5898261666297913},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.56658935546875},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.5583260655403137},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4123815596103668},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3337608575820923},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3173298239707947},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.30180299282073975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22232350707054138},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.15501374006271362},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13177433609962463},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12436255812644958}],"concepts":[{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.8769291639328003},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.6658346652984619},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6636056900024414},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6390891075134277},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.63592529296875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5993738174438477},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5898261666297913},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.56658935546875},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.5583260655403137},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4123815596103668},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3337608575820923},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3173298239707947},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.30180299282073975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22232350707054138},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.15501374006271362},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13177433609962463},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12436255812644958},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit44484.2020.9174022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit44484.2020.9174022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2011039300","https://openalex.org/W2015418199","https://openalex.org/W2078204800","https://openalex.org/W2091893102","https://openalex.org/W2109357213","https://openalex.org/W2127271355","https://openalex.org/W2134474909","https://openalex.org/W2135046866","https://openalex.org/W2140856955","https://openalex.org/W2264830470","https://openalex.org/W2294690908","https://openalex.org/W2296616510","https://openalex.org/W2982040231","https://openalex.org/W4250955649","https://openalex.org/W6610072603","https://openalex.org/W6697419950","https://openalex.org/W6769656931"],"related_works":["https://openalex.org/W2098521117","https://openalex.org/W4319793311","https://openalex.org/W2465351041","https://openalex.org/W4385694579","https://openalex.org/W2340242818","https://openalex.org/W4300881421","https://openalex.org/W2103001330","https://openalex.org/W2241396314","https://openalex.org/W3015194228","https://openalex.org/W2381127329"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,8,12,22,35,80,84,90,103,111,136,152],"novel":[3],"greedy":[4,149],"algorithm":[5,150],"to":[6,88,101,134],"recover":[7],"sparse":[9,86],"signal":[10],"from":[11],"small":[13],"number":[14],"of":[15,47,50,71,83,93],"noisy":[16],"measurements.":[17],"In":[18],"the":[19,45,51,58,61,68,110,140,147,157,166],"proposed":[20,115,148],"method,":[21],"new":[23],"support":[24,53],"index":[25],"is":[26,41,74,100,117],"identified":[27],"for":[28],"each":[29,94],"iteration,":[30],"based":[31],"on":[32,109,139],"bit-wise":[33],"maximum":[34],"posteriori":[36,91,112],"(B-MAP)":[37],"detection.":[38],"This":[39],"approach":[40],"an":[42],"optimal":[43],"in":[44,125],"sense":[46],"detecting":[48],"one":[49],"remaining":[52,95],"indices,":[54],"provided":[55],"that":[56,146],"all":[57],"indices":[59],"during":[60],"previous":[62],"iterations":[63],"are":[64],"perfectly":[65],"recovered.":[66],"Unfortunately,":[67],"exact":[69],"computation":[70],"B-MAP":[72,107],"detection":[73],"not":[75],"practical":[76],"since":[77],"it":[78],"requires":[79],"heavy":[81],"marginalization":[82],"highdimensional":[85],"vector":[87,122],"compute":[89],"probability":[92],"support.":[96],"Our":[97],"major":[98],"contribution":[99],"present":[102],"good":[104],"proxy,":[105,108],"named":[106],"probability.":[113],"The":[114],"proxy":[116],"easily":[118],"evaluated":[119],"only":[120],"using":[121],"correlations":[123],"as":[124,161],"popular":[126],"orthogonal":[127],"matching":[128],"pursuit":[129],"(OMP)":[130],"and":[131,163],"accurate":[132],"enough":[133],"represent":[135],"relative":[137],"ordering":[138],"probabilities.":[141],"Via":[142],"simulations,":[143],"we":[144],"demonstrate":[145],"yields":[151],"higher":[153],"recovery":[154],"accuracy":[155],"than":[156],"existing":[158],"benchmark":[159],"methods":[160],"OMP":[162],"MAP-OMP,":[164],"having":[165],"same":[167],"computational":[168],"complexity.":[169]},"counts_by_year":[],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
