{"id":"https://openalex.org/W2342774626","doi":"https://doi.org/10.1109/tpami.2015.2511743","title":"Scalable Robust Principal Component Analysis Using Grassmann Averages","display_name":"Scalable Robust Principal Component Analysis Using Grassmann Averages","publication_year":2015,"publication_date":"2015-12-23","ids":{"openalex":"https://openalex.org/W2342774626","doi":"https://doi.org/10.1109/tpami.2015.2511743","mag":"2342774626","pmid":"https://pubmed.ncbi.nlm.nih.gov/26731634"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2015.2511743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2015.2511743","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://orbit.dtu.dk/en/publications/20902860-ef12-42a8-b2c6-f1988a4ec5d8","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072186051","display_name":"S\u00f8ren Hauberg","orcid":"https://orcid.org/0000-0001-7223-877X"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Sren Hauberg","raw_affiliation_strings":["Section for Cognitive Systems, Technical University of Denmark"],"raw_orcid":"https://orcid.org/0000-0001-7223-877X","affiliations":[{"raw_affiliation_string":"Section for Cognitive Systems, Technical University of Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041988622","display_name":"Aasa Feragen","orcid":"https://orcid.org/0000-0002-9945-981X"},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Aasa Feragen","raw_affiliation_strings":["Department of Computer Science, University of Copenhagen, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Copenhagen, Denmark","institution_ids":["https://openalex.org/I124055696"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023920421","display_name":"Raffi Enficiaud","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Raffi Enficiaud","raw_affiliation_strings":["Software Workshop, Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Software Workshop, Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065396778","display_name":"Michael J. Black","orcid":"https://orcid.org/0000-0001-6077-4540"},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael J. Black","raw_affiliation_strings":["Perceiving Systems Department, Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perceiving Systems Department, Max Planck Institute for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3504,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.91703906,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"38","issue":"11","first_page":"2298","last_page":"2311"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9988999962806702,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8041160106658936},{"id":"https://openalex.org/keywords/robust-principal-component-analysis","display_name":"Robust principal component analysis","score":0.7899128198623657},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.772363543510437},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7461705803871155},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6377548575401306},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6300467252731323},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5006401538848877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.493673175573349},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4876326620578766},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4662906527519226},{"id":"https://openalex.org/keywords/ransac","display_name":"RANSAC","score":0.4485977590084076},{"id":"https://openalex.org/keywords/robust-statistics","display_name":"Robust statistics","score":0.440502405166626},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3866159915924072},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17657414078712463},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.15498363971710205}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8041160106658936},{"id":"https://openalex.org/C2777749129","wikidata":"https://www.wikidata.org/wiki/Q17148469","display_name":"Robust principal component analysis","level":3,"score":0.7899128198623657},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.772363543510437},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7461705803871155},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6377548575401306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6300467252731323},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5006401538848877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.493673175573349},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4876326620578766},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4662906527519226},{"id":"https://openalex.org/C114744707","wikidata":"https://www.wikidata.org/wiki/Q218533","display_name":"RANSAC","level":3,"score":0.4485977590084076},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.440502405166626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3866159915924072},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17657414078712463},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.15498363971710205},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tpami.2015.2511743","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2015.2511743","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:26731634","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26731634","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/1c084208-cc52-452f-a33e-14688d262ce7","is_oa":false,"landing_page_url":"https://researchprofiles.ku.dk/da/publications/1c084208-cc52-452f-a33e-14688d262ce7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401983","display_name":"Research at the University of Copenhagen (University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I124055696","host_organization_name":"University of Copenhagen","host_organization_lineage":["https://openalex.org/I124055696"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hauberg , S , Feragen , A , Enficiaud , R & Black , M J 2016 , ' Scalable robust principal component analysis using Grassmann averages ' , IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38 , no. 11 , pp. 2298-2311 . https://doi.org/10.1109/TPAMI.2015.2511743","raw_type":"article"},{"id":"pmh:oai:pure.atira.dk:publications/20902860-ef12-42a8-b2c6-f1988a4ec5d8","is_oa":true,"landing_page_url":"https://orbit.dtu.dk/en/publications/20902860-ef12-42a8-b2c6-f1988a4ec5d8","pdf_url":null,"source":{"id":"https://openalex.org/S4306400705","display_name":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I96673099","host_organization_name":"Technical University of Denmark","host_organization_lineage":["https://openalex.org/I96673099"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hauberg , S , Feragen , A , Enficiaud , R & Black , M J 2016 , ' Scalable Robust Principal Component Analysis Using Grassmann Averages ' , I E E E Transactions on Pattern Analysis and Machine Intelligence , vol. 38 , no. 11 , pp. 2298-2311 . https://doi.org/10.1109/TPAMI.2015.2511743","raw_type":"article"},{"id":"pmh:oai:pure.mpg.de:item_2259862","is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-002A-208D-5","pdf_url":null,"source":{"id":"https://openalex.org/S4306400654","display_name":"MPG.PuRe (Max Planck Society)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/20902860-ef12-42a8-b2c6-f1988a4ec5d8","is_oa":true,"landing_page_url":"https://orbit.dtu.dk/en/publications/20902860-ef12-42a8-b2c6-f1988a4ec5d8","pdf_url":null,"source":{"id":"https://openalex.org/S4306400705","display_name":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I96673099","host_organization_name":"Technical University of Denmark","host_organization_lineage":["https://openalex.org/I96673099"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hauberg , S , Feragen , A , Enficiaud , R & Black , M J 2016 , ' Scalable Robust Principal Component Analysis Using Grassmann Averages ' , I E E E Transactions on Pattern Analysis and Machine Intelligence , vol. 38 , no. 11 , pp. 2298-2311 . https://doi.org/10.1109/TPAMI.2015.2511743","raw_type":"article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6697271890","display_name":null,"funder_award_id":"00008721","funder_id":"https://openalex.org/F4320310490","funder_display_name":"Villum Fonden"}],"funders":[{"id":"https://openalex.org/F4320310490","display_name":"Villum Fonden","ror":"https://ror.org/007ww2d15"},{"id":"https://openalex.org/F4320322928","display_name":"Danmarks Frie Forskningsfond","ror":"https://ror.org/02sptwz63"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W56210758","https://openalex.org/W79405465","https://openalex.org/W151981251","https://openalex.org/W186085407","https://openalex.org/W258643166","https://openalex.org/W1513013675","https://openalex.org/W1557324374","https://openalex.org/W1663973292","https://openalex.org/W1736339626","https://openalex.org/W1745126362","https://openalex.org/W1904063580","https://openalex.org/W1969792798","https://openalex.org/W1970833585","https://openalex.org/W1981745143","https://openalex.org/W1992633833","https://openalex.org/W1993962865","https://openalex.org/W2018341990","https://openalex.org/W2021265981","https://openalex.org/W2030928730","https://openalex.org/W2037549374","https://openalex.org/W2050630265","https://openalex.org/W2050991582","https://openalex.org/W2053090063","https://openalex.org/W2067251371","https://openalex.org/W2072931016","https://openalex.org/W2086258274","https://openalex.org/W2088449330","https://openalex.org/W2098884960","https://openalex.org/W2103633133","https://openalex.org/W2106037979","https://openalex.org/W2123921160","https://openalex.org/W2134086356","https://openalex.org/W2140296035","https://openalex.org/W2145962650","https://openalex.org/W2150489380","https://openalex.org/W2165916500","https://openalex.org/W2166107678","https://openalex.org/W2169326908","https://openalex.org/W2177347332","https://openalex.org/W2294582956","https://openalex.org/W2489822048","https://openalex.org/W2555690212","https://openalex.org/W2753461371","https://openalex.org/W2963363076","https://openalex.org/W3013267890","https://openalex.org/W3099514962","https://openalex.org/W3103526270","https://openalex.org/W3145128584","https://openalex.org/W4212863985","https://openalex.org/W4230813007","https://openalex.org/W4238957295","https://openalex.org/W4246202668","https://openalex.org/W4246219036","https://openalex.org/W4250857377","https://openalex.org/W4293203252","https://openalex.org/W6603183647","https://openalex.org/W6607493213","https://openalex.org/W6609781850","https://openalex.org/W6637748465","https://openalex.org/W6675840925","https://openalex.org/W6676047094","https://openalex.org/W6682218280","https://openalex.org/W6684527720","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2769402452","https://openalex.org/W2091258128","https://openalex.org/W4385261474","https://openalex.org/W3037121946","https://openalex.org/W2991135868","https://openalex.org/W1983540692","https://openalex.org/W1995723671","https://openalex.org/W2164647769","https://openalex.org/W2346979549","https://openalex.org/W4387454008"],"abstract_inverted_index":{"In":[0],"large":[1],"datasets,":[2],"manual":[3],"data":[4,19,28],"verification":[5],"is":[6,35,106,150,157,205],"impossible,":[7],"and":[8,30,100,168,180],"we":[9,122],"must":[10],"expect":[11],"the":[12,42,70,76,81,103,132,191],"number":[13],"of":[14,140],"outliers":[15,38,110],"to":[16,80,109,130,159],"increase":[17],"with":[18],"size.":[20],"While":[21],"principal":[22,83],"component":[23,84],"analysis":[24],"(PCA)":[25],"can":[26,39,118],"reduce":[27],"size,":[29],"scalable":[31],"solutions":[32],"exist,":[33],"it":[34,156],"well-known":[36],"that":[37,55,75,102],"arbitrarily":[40],"corrupt":[41],"results.":[43],"Unfortunately,":[44],"state-of-the-art":[45],"approaches":[46],"for":[47,85,93,113,152,175],"robust":[48,128,141,158,188],"PCA":[49,112,189],"are":[50],"not":[51],"scalable.":[52],"We":[53,73,88,126,172,183],"note":[54],"in":[56],"a":[57,63,67,90,138,197],"zero-mean":[58],"dataset,":[59],"each":[60],"observation":[61],"spans":[62],"one-dimensional":[64],"subspace,":[65],"giving":[66],"point":[68],"on":[69,190],"Grassmann":[71,96,134,146],"manifold.":[72],"show":[74,101,184],"average":[77],"subspace":[78,104],"corresponds":[79],"leading":[82],"Gaussian":[86],"data.":[87],"provide":[89],"simple":[91],"algorithm":[92,163],"computing":[94],"this":[95],"Average":[97,135,147],"(":[98,148],"GA),":[99],"estimate":[105],"less":[107],"sensitive":[108],"than":[111],"general":[114],"distributions.":[115],"Because":[116],"averages":[117],"be":[119],"efficiently":[120],"computed,":[121],"immediately":[123],"gain":[124],"scalability.":[125],"exploit":[127],"averaging":[129],"formulate":[131],"Robust":[133],"(RGA)":[136],"as":[137],"form":[139],"PCA.":[142],"The":[143,162],"resulting":[144],"Trimmed":[145],"TGA)":[149],"appropriate":[151],"computer":[153],"vision":[154],"because":[155],"pixel":[160],"outliers.":[161],"has":[164],"linear":[165],"computational":[166],"complexity":[167],"minimal":[169],"memory":[170],"requirements.":[171],"demonstrate":[173],"TGA":[174],"background":[176],"modeling,":[177],"video":[178],"restoration,":[179],"shadow":[181],"removal.":[182],"scalability":[185],"by":[186],"performing":[187],"entire":[192],"Star":[193],"Wars":[194],"IV":[195],"movie;":[196],"task":[198],"beyond":[199],"any":[200],"current":[201],"method.":[202],"Source":[203],"code":[204],"available":[206],"online.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}