{"id":"https://openalex.org/W2397738293","doi":"https://doi.org/10.1109/icassp.2016.7472442","title":"Outlier-robust recovery of low-rank positive semidefinite matrices from magnitude measurements","display_name":"Outlier-robust recovery of low-rank positive semidefinite matrices from magnitude measurements","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2397738293","doi":"https://doi.org/10.1109/icassp.2016.7472442","mag":"2397738293"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5062598400","display_name":"Yue Sun","orcid":"https://orcid.org/0000-0001-8954-0158"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Sun","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011866457","display_name":"Yuan\u2010Xin Li","orcid":"https://orcid.org/0000-0003-4136-0961"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanxin Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The Ohio State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The Ohio State University, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053809095","display_name":"Yuejie Chi","orcid":"https://orcid.org/0000-0002-6766-5459"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuejie Chi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The Ohio State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The Ohio State University, USA","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4069","last_page":"4073"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9965999722480774,"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"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9922999739646912,"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/subgradient-method","display_name":"Subgradient method","score":0.6980036497116089},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6942889094352722},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5985120534896851},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5745173692703247},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.5514590740203857},{"id":"https://openalex.org/keywords/positive-definite-matrix","display_name":"Positive-definite matrix","score":0.535814106464386},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.4878954589366913},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.48496079444885254},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.44880276918411255},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43962612748146057},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4387931823730469},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4283943176269531},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33220210671424866},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.3110980987548828},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.2884831428527832},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18243888020515442},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.07737568020820618},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.06496509909629822}],"concepts":[{"id":"https://openalex.org/C158968445","wikidata":"https://www.wikidata.org/wiki/Q7631150","display_name":"Subgradient method","level":2,"score":0.6980036497116089},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6942889094352722},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5985120534896851},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5745173692703247},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.5514590740203857},{"id":"https://openalex.org/C49712288","wikidata":"https://www.wikidata.org/wiki/Q77601250","display_name":"Positive-definite matrix","level":3,"score":0.535814106464386},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.4878954589366913},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.48496079444885254},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.44880276918411255},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43962612748146057},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4387931823730469},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4283943176269531},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33220210671424866},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.3110980987548828},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2884831428527832},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18243888020515442},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.07737568020820618},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.06496509909629822},{"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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":23,"referenced_works":["https://openalex.org/W221278985","https://openalex.org/W2003753589","https://openalex.org/W2078397124","https://openalex.org/W2124252039","https://openalex.org/W2133105246","https://openalex.org/W2142949395","https://openalex.org/W2145080587","https://openalex.org/W2145962650","https://openalex.org/W2155434899","https://openalex.org/W2169501582","https://openalex.org/W2193937378","https://openalex.org/W2224829387","https://openalex.org/W2261161581","https://openalex.org/W2542482481","https://openalex.org/W2962909343","https://openalex.org/W2963863416","https://openalex.org/W3102206315","https://openalex.org/W3104624268","https://openalex.org/W3148325197","https://openalex.org/W6608805005","https://openalex.org/W6637311487","https://openalex.org/W6687211498","https://openalex.org/W6689469898"],"related_works":["https://openalex.org/W2359120930","https://openalex.org/W2168915192","https://openalex.org/W2140018151","https://openalex.org/W3084689723","https://openalex.org/W3161275408","https://openalex.org/W3088056528","https://openalex.org/W2811183536","https://openalex.org/W4312559935","https://openalex.org/W2137785954","https://openalex.org/W3104665944"],"abstract_inverted_index":{"We":[0,31],"address":[1],"the":[2,22,26,38,43,46,53,76,84,97,100],"problem":[3],"of":[4,15,28,45,62,75,96,99],"estimating":[5],"a":[6,13,33,58,73,105,110],"low-rank":[7],"positive":[8],"semidefinite":[9],"(PSD)":[10],"matrix":[11,40,61,102],"from":[12,64],"set":[14],"magnitude":[16],"measurements":[17,67,77],"that":[18,36,41,52,117],"are":[19],"quadratic":[20],"in":[21,25],"sensing":[23],"vectors":[24],"presence":[27],"arbitrary":[29,81],"outliers.":[30,82],"propose":[32,109],"parameter-free":[34],"algorithm":[35,54,112],"seeks":[37],"PSD":[39,60,101],"minimizes":[42],"\u21131-norm":[44],"measurement":[47],"residual.":[48],"It":[49],"is":[50,78,86,103],"shown":[51],"can":[55],"exactly":[56],"recover":[57],"rank-r":[59],"size-n":[63],"O":[65],"(nr2)":[66],"with":[68],"high":[69],"probability,":[70],"even":[71],"when":[72],"fraction":[74],"corrupted":[79],"by":[80],"Furthermore,":[83],"recovery":[85],"also":[87],"robust":[88],"to":[89],"bounded":[90],"noise.":[91],"When":[92],"an":[93],"upper":[94],"bound":[95],"rank":[98],"known":[104],"priori,":[106],"we":[107],"further":[108],"non-convex":[111],"based":[113],"on":[114],"subgradient":[115],"descent":[116],"demonstrates":[118],"superior":[119],"empirical":[120],"performance.":[121]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}