{"id":"https://openalex.org/W4388624327","doi":"https://doi.org/10.1109/tit.2023.3332084","title":"Improved Support Recovery in Universal 1-bit Compressed Sensing","display_name":"Improved Support Recovery in Universal 1-bit Compressed Sensing","publication_year":2023,"publication_date":"2023-11-13","ids":{"openalex":"https://openalex.org/W4388624327","doi":"https://doi.org/10.1109/tit.2023.3332084"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2023.3332084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2023.3332084","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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 Information Theory","raw_type":"journal-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/A5079387295","display_name":"Namiko Matsumoto","orcid":"https://orcid.org/0000-0003-4861-5496"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Namiko Matsumoto","raw_affiliation_strings":["Department of Computer Science and Engineering and Halicioglu Data Science Institute, University of California at San Diego, La Jolla, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-8777-4233","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering and Halicioglu Data Science Institute, University of California at San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051046818","display_name":"Arya Mazumdar","orcid":"https://orcid.org/0000-0003-4605-7996"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arya Mazumdar","raw_affiliation_strings":["Department of Computer Science and Engineering and Halicioglu Data Science Institute, University of California at San Diego, La Jolla, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-4605-7996","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering and Halicioglu Data Science Institute, University of California at San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047754690","display_name":"Soumyabrata Pal","orcid":"https://orcid.org/0000-0003-2949-3761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soumyabrata Pal","raw_affiliation_strings":["Google Research, Bengaluru, India"],"raw_orcid":"https://orcid.org/0000-0003-2949-3761","affiliations":[{"raw_affiliation_string":"Google Research, Bengaluru, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.126,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.72161239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"70","issue":"2","first_page":"1453","last_page":"1472"},"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":0.9986000061035156,"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"}},{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/compressed-sensing","display_name":"Compressed sensing","score":0.7881903648376465},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5343137979507446},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.4510273039340973},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.425432950258255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34896552562713623},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3295809328556061},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.13335326313972473},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.10211837291717529}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7881903648376465},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5343137979507446},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4510273039340973},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.425432950258255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34896552562713623},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3295809328556061},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.13335326313972473},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.10211837291717529}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tit.2023.3332084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2023.3332084","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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 Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1754370423","display_name":"CAREER: Reliability in Large-Scale Storage","funder_award_id":"2127929","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3930916604","display_name":"Collaborative Research: EnCORE: Institute for Emerging CORE Methods in Data Science","funder_award_id":"2217058","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5121931882","display_name":"CIF: Small: New Directions in Clustering: Interactive Algorithms and Statistical Models","funder_award_id":"2133484","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W112929769","https://openalex.org/W1600581830","https://openalex.org/W1971372570","https://openalex.org/W2009085022","https://openalex.org/W2012584108","https://openalex.org/W2047765680","https://openalex.org/W2060430274","https://openalex.org/W2096573287","https://openalex.org/W2112038498","https://openalex.org/W2121716262","https://openalex.org/W2145096794","https://openalex.org/W2164604747","https://openalex.org/W2286685332","https://openalex.org/W2521231262","https://openalex.org/W2963401889","https://openalex.org/W2963991930","https://openalex.org/W2964322027","https://openalex.org/W2982634139","https://openalex.org/W3099510449","https://openalex.org/W4250955649","https://openalex.org/W4289713091","https://openalex.org/W4298949291","https://openalex.org/W4313227172","https://openalex.org/W6607403468","https://openalex.org/W6637770602","https://openalex.org/W6751121997","https://openalex.org/W6767352173","https://openalex.org/W6773747557","https://openalex.org/W6799298882"],"related_works":["https://openalex.org/W1979597421","https://openalex.org/W2158224665","https://openalex.org/W2007980826","https://openalex.org/W2051487156","https://openalex.org/W2379589510","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W2077600819","https://openalex.org/W4300044672","https://openalex.org/W2023089748"],"abstract_inverted_index":{"One-bit":[0],"compressed":[1],"sensing":[2],"(1bCS)":[3],"is":[4,117,149,203,216,268],"an":[5,48],"extremely":[6],"quantized":[7,32],"signal":[8,30,86,107,126],"acquisition":[9],"method":[10],"that":[11,140,197,226],"has":[12],"been":[13],"proposed":[14],"and":[15,157,187,296],"studied":[16],"rigorously":[17],"in":[18,93,175,227,285],"the":[19,41,53,62,84,99,102,106,163,167,198,209],"past":[20],"decade.":[21],"In":[22,263],"1bCS,":[23],"linear":[24,59,79],"samples":[25],"of":[26,40,52,70,75,101,114,138,185,192,201,212,251,289],"a":[27,72,77,109,248],"high":[28],"dimensional":[29],"are":[31,155,240,282,294],"to":[33,88,97,135,173],"only":[34],"one":[35,136],"bit":[36],"per":[37],"sample":[38],"(sign":[39],"measurement).":[42],"The":[43,112],"extreme":[44],"quantization":[45],"makes":[46],"it":[47,65,148],"interesting":[49],"case":[50],"study":[51],"more":[54,214],"general":[55],"single-index":[56],"or":[57,81,104,257],"generalized":[58],"models.":[60],"At":[61],"same":[63],"time":[64],"can":[66],"also":[67,122,224,283],"be":[68,89,233],"thought":[69],"as":[71,254],"\u2018design\u2019":[73],"version":[74],"learning":[76],"binary":[78],"classifier":[80],"halfspace-learning.":[82],"Assuming":[83],"original":[85],"vector":[87],"sparse,":[90],"existing":[91],"results":[92,239,275],"1bCS":[94,133],"either":[95],"aim":[96],"find":[98],"support":[100,118,160,181,280],"vector,":[103],"approximate":[105,125,180,279],"allowing":[108],"small":[110],"error.":[111],"focus":[113],"this":[115,176,286],"paper":[116],"recovery,":[119,213],"which":[120],"often":[121],"computationally":[123],"facilitate":[124],"recovery.":[127,237],"A":[128],"universal":[129,236,245,277],"measurement":[130],"matrix":[131],"for":[132,142,159,235],"refers":[134],"set":[137],"measurements":[139,154],"work":[141,177],"all":[143],"sparse":[144],"signals.":[145],"With":[146],"universality,":[147],"known":[150],"that$\\tilde":[151],"{\\Theta":[152],"}(k^{2})~1$bCS":[153],"necessary":[156,234],"sufficient":[158],"recovery":[161,182,189,202,246,267,281,292],"(where$k$denotes":[162],"sparsity).":[164],"To":[165],"improve":[166],"dependence":[168],"on":[169,276],"sparsity":[170],"from":[171],"quadratic":[172],"linear,":[174],"we":[178,243],"propose":[179],"(allowing$\\epsilon":[183,190],"&gt;0$proportion":[184],"errors),":[186],"superset":[188,266],"$proportion":[191],"false":[193],"positives).":[194],"We":[195,223],"show":[196,225],"first":[199],"type":[200,211],"possible":[204,217,241,269],"with$\\tilde":[205,218],"{O}(k/\\epsilon":[206,272],")$measurements,":[207],"while":[208],"later":[210],"challenging,":[215],"{O}(\\max":[219],"\\{k/\\epsilon":[220],",k^{3/2}\\})":[221],"^{^{^{^{}}}}$measurements.":[222],"both":[228,264],"cases$\\Omega":[229],"(k/\\epsilon":[230],")$measurements":[231],"would":[232],"Improved":[238],"if":[242],"consider":[244],"within":[247],"restricted":[249],"class":[250],"signals,":[252,256],"such":[253],"rational":[255],"signals":[258],"with":[259,270],"bounded":[260],"dynamic":[261],"range.":[262],"cases":[265],"only$\\tilde":[271],")$measurements.":[273],"Other":[274],"but":[278],"provided":[284],"paper.":[287],"All":[288],"our":[290],"main":[291],"algorithms":[293],"simple":[295],"polynomial-time.":[297]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
