{"id":"https://openalex.org/W3020879211","doi":"https://doi.org/10.1145/3341105.3374013","title":"Analysis of label noise in graph-based semi-supervised learning","display_name":"Analysis of label noise in graph-based semi-supervised learning","publication_year":2020,"publication_date":"2020-03-30","ids":{"openalex":"https://openalex.org/W3020879211","doi":"https://doi.org/10.1145/3341105.3374013","mag":"3020879211"},"language":"en","primary_location":{"id":"doi:10.1145/3341105.3374013","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341105.3374013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 35th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.12966","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bruno Klaus de Aquino Afonso","orcid":null},"institutions":[{"id":"https://openalex.org/I88273585","display_name":"Universidade Federal de S\u00e3o Paulo","ror":"https://ror.org/02k5swt12","country_code":"BR","type":"education","lineage":["https://openalex.org/I88273585"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Bruno Klaus de Aquino Afonso","raw_affiliation_strings":["Federal University of S\u00e3o Paulo, S\u00e3o Jos\u00e9 dos Campos, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Paulo, S\u00e3o Jos\u00e9 dos Campos, Brazil","institution_ids":["https://openalex.org/I88273585"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lilian Berton","orcid":null},"institutions":[{"id":"https://openalex.org/I88273585","display_name":"Universidade Federal de S\u00e3o Paulo","ror":"https://ror.org/02k5swt12","country_code":"BR","type":"education","lineage":["https://openalex.org/I88273585"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lilian Berton","raw_affiliation_strings":["Federal University of S\u00e3o Paulo, S\u00e3o Jos\u00e9 dos Campos, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Paulo, S\u00e3o Jos\u00e9 dos Campos, Brazil","institution_ids":["https://openalex.org/I88273585"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I88273585"],"apc_list":null,"apc_paid":null,"fwci":0.9599,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.80675751,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1127","last_page":"1134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12535","display_name":"Machine Learning and Data Classification","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/noise","display_name":"Noise (video)","score":0.6384000182151794},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5148000121116638},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49219998717308044},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4408000111579895},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.4341000020503998},{"id":"https://openalex.org/keywords/laplace-operator","display_name":"Laplace operator","score":0.40959998965263367},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.38179999589920044},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.37940001487731934}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6384000182151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6363999843597412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6347000002861023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5410000085830688},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5148000121116638},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49219998717308044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4408000111579895},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.4341000020503998},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.34389999508857727},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3375000059604645},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2581999897956848},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3341105.3374013","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341105.3374013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 35th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2009.12966","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.12966","pdf_url":"https://arxiv.org/pdf/2009.12966","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.12966","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.12966","pdf_url":"https://arxiv.org/pdf/2009.12966","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1611185808","https://openalex.org/W1968555645","https://openalex.org/W2034841618","https://openalex.org/W2056021151","https://openalex.org/W2069222105","https://openalex.org/W2077033677","https://openalex.org/W2079788575","https://openalex.org/W2099791118","https://openalex.org/W2102348129","https://openalex.org/W2126523478","https://openalex.org/W2141923507","https://openalex.org/W2167460663","https://openalex.org/W2169600004","https://openalex.org/W2486799789","https://openalex.org/W2559034748","https://openalex.org/W2772144603","https://openalex.org/W2895792207","https://openalex.org/W2964149605","https://openalex.org/W2964325543","https://openalex.org/W2991772305","https://openalex.org/W4234400701"],"related_works":[],"abstract_inverted_index":{"In":[0,83],"machine":[1],"learning,":[2],"one":[3],"must":[4],"acquire":[5],"labels":[6,58,106,197],"to":[7,16,18,93,103,117,183],"help":[8],"supervise":[9],"a":[10],"model":[11],"that":[12,37,48,98,107],"will":[13],"be":[14,26],"able":[15,102,182],"generalize":[17],"unseen":[19],"data.":[20],"However,":[21],"the":[22,35,54,57,60,79,150,156,173,185,193,200,210],"labeling":[23],"process":[24],"can":[25],"tedious,":[27],"long,":[28],"costly,":[29],"and":[30,59,88,111,131,135,161],"error-prone.":[31],"It":[32],"is":[33,42,96,175],"often":[34],"case":[36],"most":[38,72],"of":[39,123,152,158,195],"our":[40,99],"data":[41,62,160,211],"unlabeled.":[43],"Semi-supervised":[44],"learning":[45],"(SSL)":[46],"alleviates":[47],"by":[49],"making":[50],"strong":[51],"assumptions":[52],"about":[53],"relation":[55],"between":[56],"input":[61],"distribution.":[63],"This":[64],"paradigm":[65],"has":[66],"been":[67],"successful":[68],"in":[69],"practice,":[70],"but":[71],"SSL":[73,178],"algorithms":[74,100],"end":[75],"up":[76],"fully":[77],"trusting":[78],"few":[80,110],"available":[81,196],"labels.":[82],"real":[84],"life,":[85],"both":[86,109],"humans":[87],"automated":[89],"systems":[90],"are":[91,101,108,181],"prone":[92],"mistakes;":[94],"it":[95],"essential":[97],"work":[104,115],"with":[105,177],"also":[112],"unreliable.":[113],"Our":[114,168],"aims":[116],"perform":[118],"an":[119],"extensive":[120],"empirical":[121],"evaluation":[122],"existing":[124],"graph-based":[125],"semi-supervised":[126],"algorithms,":[127],"like":[128],"Gaussian":[129],"Fields":[130],"Harmonic":[132],"Functions,":[133],"Local":[134],"Global":[136],"Consistency,":[137],"Laplacian":[138,201],"Eigenmaps,":[139],"Graph":[140],"Transduction":[141],"Through":[142],"Alternating":[143],"Minimization.":[144],"To":[145],"do":[146],"that,":[147,171],"we":[148,180],"compare":[149],"accuracy":[151],"classifiers":[153],"while":[154],"varying":[155],"amount":[157],"labeled":[159],"label":[162,207],"noise":[163],"for":[164],"many":[165],"different":[166],"samples.":[167],"results":[169],"show":[170],"if":[172],"dataset":[174],"consistent":[176],"assumptions,":[179],"detect":[184],"noisiest":[186],"instances,":[187],"although":[188],"this":[189],"gets":[190],"harder":[191],"when":[192,209],"number":[194],"decreases.":[198],"Also,":[199],"Eigenmaps":[202],"algorithm":[203],"performed":[204],"better":[205],"than":[206],"propagation":[208],"came":[212],"from":[213],"high-dimensional":[214],"clusters.":[215]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-05-13T00:00:00"}
