{"id":"https://openalex.org/W3015876643","doi":"https://doi.org/10.1109/icassp40776.2020.9054451","title":"Robustness of Sparse Bayesian Learning in Correlated Environments","display_name":"Robustness of Sparse Bayesian Learning in Correlated Environments","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015876643","doi":"https://doi.org/10.1109/icassp40776.2020.9054451","mag":"3015876643"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9054451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5075263254","display_name":"Rohan R. Pote","orcid":"https://orcid.org/0000-0003-2987-1531"},"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":true,"raw_author_name":"Rohan R. Pote","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, San Diego"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108088237","display_name":"Bhaskar D. Rao","orcid":null},"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":"Bhaskar D. Rao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, San Diego"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075263254"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":1.3638,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.8078341,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"9100","last_page":"9104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9994000196456909,"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/T11698","display_name":"Underwater Acoustics Research","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7837562561035156},{"id":"https://openalex.org/keywords/uncorrelated","display_name":"Uncorrelated","score":0.6452692151069641},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6335435509681702},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6173136830329895},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5864437222480774},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5108460783958435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.509318470954895},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.507356584072113},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.493792861700058},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4841385781764984},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4609224796295166},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41786617040634155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4102582633495331},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.39556506276130676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3257511258125305},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31847378611564636},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10309943556785583}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7837562561035156},{"id":"https://openalex.org/C169345407","wikidata":"https://www.wikidata.org/wiki/Q8216221","display_name":"Uncorrelated","level":2,"score":0.6452692151069641},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6335435509681702},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6173136830329895},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5864437222480774},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5108460783958435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.509318470954895},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.507356584072113},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.493792861700058},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4841385781764984},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4609224796295166},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41786617040634155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4102582633495331},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39556506276130676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3257511258125305},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31847378611564636},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10309943556785583},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9054451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1972767298","https://openalex.org/W1994477821","https://openalex.org/W2028781966","https://openalex.org/W2065919982","https://openalex.org/W2099464104","https://openalex.org/W2139313032","https://openalex.org/W2148154358","https://openalex.org/W2162654459","https://openalex.org/W2759547991","https://openalex.org/W2889156138","https://openalex.org/W2937977811","https://openalex.org/W2952350176","https://openalex.org/W3100410537","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2105321464","https://openalex.org/W2887774187","https://openalex.org/W2388220555","https://openalex.org/W1554844486","https://openalex.org/W1520875569","https://openalex.org/W3048739257","https://openalex.org/W2963604926","https://openalex.org/W2033178790","https://openalex.org/W2199957582","https://openalex.org/W1665563134"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"analyse":[4],"the":[5,46,82,109,118,129,144],"performance":[6,113],"of":[7,40,114],"Sparse":[8],"Bayesian":[9],"Learning":[10],"(SBL)":[11],"in":[12],"an":[13,101],"environment":[14],"with":[15],"correlated":[16,29],"sources.":[17,30],"We":[18],"provide":[19],"two":[20,119],"new":[21],"perspectives":[22],"to":[23,62,71,81,99,124,135,142],"understand":[24],"SBL's":[25,73,137],"strategy":[26],"for":[27,117],"handling":[28],"Using":[31],"a":[32],"Minimum":[33],"Power":[34],"Distortionless":[35],"Response":[36],"(MPDR)":[37],"beamformer-based":[38],"perspective":[39,68],"SBL,":[41,58],"it":[42,93],"is":[43,69,79,94,122],"shown":[44,95],"that":[45,96,106],"measured":[47],"signal":[48],"covariance":[49,104],"strucure":[50],"based":[51,67],"on":[52,112],"uncorrelated":[53,102],"sources":[54,148],"assumption":[55],"used":[56],"by":[57],"infact":[59],"provides":[60],"immunity":[61],"source":[63,103,120],"correlation.":[64],"The":[65],"MPDR":[66],"extended":[70],"formulate":[72],"misspecified":[74,77,115],"model.":[75],"This":[76],"model":[78,145],"compared":[80],"underlying":[83],"true":[84],"data":[85],"distribution":[86],"using":[87],"Kullback-Leibler":[88],"(KL)":[89],"divergence":[90],"metric":[91],"and":[92],"SBL":[97],"attempts":[98],"find":[100],"matrix":[105],"best":[107],"fits":[108],"data.":[110],"Theory":[111],"models":[116],"case":[121],"studied":[123],"gain":[125],"further":[126],"insights":[127],"into":[128],"problem.":[130],"Numerical":[131],"results":[132],"are":[133,149],"provided":[134],"demonstrate":[136],"robustness":[138],"as":[139,141],"well":[140],"quantify":[143],"misfit":[146],"when":[147],"correlated.":[150]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}