{"id":"https://openalex.org/W3016087947","doi":"https://doi.org/10.1109/icassp40776.2020.9052959","title":"Fast Block-Sparse Estimation for Vector Networks","display_name":"Fast Block-Sparse Estimation for Vector Networks","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3016087947","doi":"https://doi.org/10.1109/icassp40776.2020.9052959","mag":"3016087947"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9052959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9052959","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/A5048749917","display_name":"Zuogong Yue","orcid":"https://orcid.org/0000-0001-8457-2900"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Zuogong Yue","raw_affiliation_strings":["School of Electrical Engineering and Telecommunications, UNSW, Sydney, AUSTRALIA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, UNSW, Sydney, AUSTRALIA","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023783147","display_name":"Padmavathi Sundaram","orcid":null},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]},{"id":"https://openalex.org/I4210127055","display_name":"Athinoula A. Martinos Center for Biomedical Imaging","ror":"https://ror.org/032q5ym94","country_code":"US","type":"facility","lineage":["https://openalex.org/I136199984","https://openalex.org/I4210087915","https://openalex.org/I4210127055","https://openalex.org/I48633490","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Padmavathi Sundaram","raw_affiliation_strings":["Harvard Medical School, Martinos Center for Biomedical Imaging, Charlestown, MA, USA"],"affiliations":[{"raw_affiliation_string":"Harvard Medical School, Martinos Center for Biomedical Imaging, Charlestown, MA, USA","institution_ids":["https://openalex.org/I4210127055","https://openalex.org/I136199984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091154507","display_name":"Victor Solo","orcid":"https://orcid.org/0000-0002-5123-7708"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Victor Solo","raw_affiliation_strings":["School of Electrical Engineering and Telecommunications, UNSW, Sydney, AUSTRALIA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, UNSW, Sydney, AUSTRALIA","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048749917"],"corresponding_institution_ids":["https://openalex.org/I31746571"],"apc_list":null,"apc_paid":null,"fwci":0.5282,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.589204,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5510","last_page":"5514"},"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9991999864578247,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6507713794708252},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6359988451004028},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5560328364372253},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.518427848815918},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5164840221405029},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.49749115109443665},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4896546006202698},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.47318458557128906},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4315067529678345},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.42690038681030273},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.4105266332626343},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27307963371276855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15104466676712036},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1377127468585968},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.11729860305786133},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08100026845932007}],"concepts":[{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6507713794708252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6359988451004028},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5560328364372253},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.518427848815918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5164840221405029},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.49749115109443665},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4896546006202698},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.47318458557128906},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4315067529678345},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.42690038681030273},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.4105266332626343},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27307963371276855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15104466676712036},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1377127468585968},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.11729860305786133},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08100026845932007},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9052959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9052959","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W167498579","https://openalex.org/W1706044825","https://openalex.org/W2010824638","https://openalex.org/W2068753436","https://openalex.org/W2116805437","https://openalex.org/W2132555912","https://openalex.org/W2133396774","https://openalex.org/W2139811697","https://openalex.org/W2151128232","https://openalex.org/W2161733758","https://openalex.org/W2167826316","https://openalex.org/W2890892020","https://openalex.org/W2952754342","https://openalex.org/W2962826552","https://openalex.org/W3098834468","https://openalex.org/W4229706427","https://openalex.org/W4230100480","https://openalex.org/W4294651009","https://openalex.org/W6606791906","https://openalex.org/W6637535267","https://openalex.org/W6677437772","https://openalex.org/W6679719908","https://openalex.org/W6681693836","https://openalex.org/W6682227116","https://openalex.org/W6684022278"],"related_works":["https://openalex.org/W2921280830","https://openalex.org/W2126916073","https://openalex.org/W2887132723","https://openalex.org/W2572601863","https://openalex.org/W2038723318","https://openalex.org/W1679731869","https://openalex.org/W1670628120","https://openalex.org/W3041177925","https://openalex.org/W2886934452","https://openalex.org/W2024369332"],"abstract_inverted_index":{"While":[0],"there":[1],"is":[2,84,110],"now":[3],"a":[4,17,33,75,106,125],"significant":[5],"literature":[6],"on":[7,118],"sparse":[8],"inverse":[9,64],"covariance":[10,65],"estimation,":[11],"all":[12],"that":[13,89],"literature,":[14],"with":[15,24],"only":[16,23,85],"couple":[18],"of":[19,52,131],"exceptions,":[20],"has":[21,114],"dealt":[22],"univariate":[25,34],"(or":[26,58],"scalar)":[27],"networks":[28,60],"where":[29],"each":[30,42],"node":[31,43],"carries":[32],"signal.":[35],"However":[36],"in":[37],"many,":[38],"perhaps":[39],"most,":[40],"applications,":[41],"may":[44],"carry":[45],"multivariate":[46,57],"signals":[47],"representing":[48],"multi-attribute":[49],"data,":[50],"possibly":[51],"different":[53],"dimensions.":[54],"Modelling":[55],"such":[56],"vector)":[59],"requires":[61],"fitting":[62],"block-sparse":[63],"matrices.":[66],"Here":[67,98],"we":[68,99],"achieve":[69],"maximal":[70],"block":[71],"sparsity":[72],"by":[73,128],"maximizing":[74],"block-l":[76],"<sub":[77],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[78],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">0</sub>":[79],"-sparse":[80],"penalized":[81],"likelihood.":[82],"There":[83],"one":[86],"previous":[87],"algorithm":[88,108],"already":[90],"does":[91,95],"this,":[92],"but":[93],"it":[94],"not":[96],"scale.":[97],"address":[100],"key":[101],"computational":[102,126],"bottlenecks":[103],"and":[104,113],"develop":[105],"new":[107],"which":[109],"much":[111],"faster":[112],"massively":[115],"reduced":[116],"requirements":[117],"matrix":[119],"conditioning.":[120],"A":[121],"benchmark":[122],"study":[123],"shows":[124],"speed-up":[127],"many":[129],"orders":[130],"magnitude.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
