{"id":"https://openalex.org/W4392931628","doi":"https://doi.org/10.1109/icassp48485.2024.10447865","title":"Large Covariance Matrix Estimation Based on Factor Models via Nonconvex Optimization","display_name":"Large Covariance Matrix Estimation Based on Factor Models via Nonconvex Optimization","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392931628","doi":"https://doi.org/10.1109/icassp48485.2024.10447865"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 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/A5005886265","display_name":"Shanshan Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shanshan Zou","raw_affiliation_strings":["ShanghaiTech University,School of Information Science and Technology,Shanghai,China","School of Information Science and Technology, ShanghaiTech University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I30809798"]},{"raw_affiliation_string":"School of Information Science and Technology, ShanghaiTech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074721617","display_name":"Ziping Zhao","orcid":"https://orcid.org/0000-0002-8668-6263"},"institutions":[{"id":"https://openalex.org/I30809798","display_name":"ShanghaiTech University","ror":"https://ror.org/030bhh786","country_code":"CN","type":"education","lineage":["https://openalex.org/I30809798"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziping Zhao","raw_affiliation_strings":["ShanghaiTech University,School of Information Science and Technology,Shanghai,China","School of Information Science and Technology, ShanghaiTech University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I30809798"]},{"raw_affiliation_string":"School of Information Science and Technology, ShanghaiTech University, Shanghai, China","institution_ids":["https://openalex.org/I30809798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005886265"],"corresponding_institution_ids":["https://openalex.org/I30809798"],"apc_list":null,"apc_paid":null,"fwci":1.1895,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72132325,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"9656","last_page":"9660"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9987000226974487,"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":0.9976000189781189,"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/mathematics","display_name":"Mathematics","score":0.5892584919929504},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5749003887176514},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5679150819778442},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5601751208305359},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.48803892731666565},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.4836200773715973},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4761400520801544},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.46072155237197876},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.43036335706710815},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4108063578605652},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3241100311279297},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12201392650604248}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5892584919929504},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5749003887176514},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5679150819778442},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5601751208305359},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.48803892731666565},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.4836200773715973},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4761400520801544},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.46072155237197876},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.43036335706710815},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4108063578605652},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3241100311279297},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12201392650604248},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 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":32,"referenced_works":["https://openalex.org/W216325278","https://openalex.org/W1990512452","https://openalex.org/W2010681793","https://openalex.org/W2040373108","https://openalex.org/W2066218102","https://openalex.org/W2066306397","https://openalex.org/W2068135051","https://openalex.org/W2073681337","https://openalex.org/W2076818396","https://openalex.org/W2098056745","https://openalex.org/W2125295547","https://openalex.org/W2154514661","https://openalex.org/W2162328283","https://openalex.org/W2320827350","https://openalex.org/W2414808552","https://openalex.org/W2582533304","https://openalex.org/W2586353914","https://openalex.org/W2898076323","https://openalex.org/W2912400541","https://openalex.org/W2933407394","https://openalex.org/W3099609308","https://openalex.org/W3102974460","https://openalex.org/W3106324661","https://openalex.org/W3123805579","https://openalex.org/W3192692954","https://openalex.org/W4212863985","https://openalex.org/W4371805675","https://openalex.org/W4386590847","https://openalex.org/W6640349290","https://openalex.org/W6674784248","https://openalex.org/W6675840925","https://openalex.org/W6777674752"],"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":{"In":[0],"this":[1],"paper,":[2],"we":[3,94],"study":[4],"the":[5,14,21,39,55,61,84,107,134,140],"problem":[6,41],"of":[7,29,110,136],"large":[8],"covariance":[9,22,114],"matrix":[10,23,32],"estimation":[11,40],"based":[12],"on":[13],"factor":[15],"model":[16],"assumption,":[17],"in":[18],"which":[19],"case":[20],"is":[24,51,77],"represented":[25],"by":[26,79],"a":[27,30,34,43,74,99],"combination":[28],"low-rank":[31],"and":[33,46,72,143],"sparse":[35],"matrix.":[36],"We":[37,82],"formulate":[38],"as":[42],"nonconvex":[44,86],"problem,":[45],"an":[47],"iterative":[48,80],"optimization":[49],"algorithm":[50,58,118,138],"proposed":[52],"to":[53,104,126],"obtain":[54],"estimator.":[56],"The":[57],"starts":[59],"at":[60],"widely":[62],"used":[63],"optimization-free":[64],"estimator":[65,76,87],"called":[66],"principal":[67],"orthogonal":[68],"complement":[69],"thresholding":[70],"(POET),":[71],"then":[73],"refined":[75],"obtained":[78,85],"optimization.":[81],"name":[83],"POET":[88],"with":[89],"refining":[90],"iteratively":[91],"(POETRY).":[92],"Theoretically,":[93],"prove":[95],"that":[96],"POETRY":[97],"achieves":[98],"superior":[100],"statistical":[101],"rate":[102,109],"compared":[103,125],"POET,":[105],"matching":[106],"minimax":[108],"convergence":[111],"for":[112],"factor-based":[113],"estimation.":[115],"Additionally,":[116],"our":[117,137,145],"exhibits":[119],"significantly":[120],"lower":[121],"per-iteration":[122],"computational":[123],"complexity":[124],"existing":[127],"convex":[128],"relaxation-based":[129],"methods.":[130],"Numerical":[131],"experiments":[132],"validate":[133],"superiority":[135],"over":[139],"state-of-the-art":[141],"ones":[142],"corroborate":[144],"theory.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
