{"id":"https://openalex.org/W3028324573","doi":"https://doi.org/10.1109/tcyb.2020.2990908","title":"A Self-Paced Regularization Framework for Partial-Label Learning","display_name":"A Self-Paced Regularization Framework for Partial-Label Learning","publication_year":2020,"publication_date":"2020-05-18","ids":{"openalex":"https://openalex.org/W3028324573","doi":"https://doi.org/10.1109/tcyb.2020.2990908","mag":"3028324573","pmid":"https://pubmed.ncbi.nlm.nih.gov/32452795"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2020.2990908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2020.2990908","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gengyu Lyu","orcid":"https://orcid.org/0000-0001-8382-2007"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gengyu Lyu","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8382-2007","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Songhe Feng","orcid":"https://orcid.org/0000-0002-5922-9358"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songhe Feng","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5922-9358","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tao Wang","orcid":"https://orcid.org/0000-0003-2369-2129"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Wang","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2369-2129","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":null,"display_name":"Congyan Lang","orcid":"https://orcid.org/0000-0001-6059-7943"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Congyan Lang","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6059-7943","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":2.4771,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.91272079,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"52","issue":"2","first_page":"899","last_page":"911"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9322999715805054,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9322999715805054,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.023099999874830246,"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/T10057","display_name":"Face and Expression Recognition","score":0.003700000001117587,"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/robustness","display_name":"Robustness (evolution)","score":0.5440999865531921},{"id":"https://openalex.org/keywords/iterative-learning-control","display_name":"Iterative learning control","score":0.4880000054836273},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4221000075340271},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.40939998626708984},{"id":"https://openalex.org/keywords/instance-based-learning","display_name":"Instance-based learning","score":0.39959999918937683},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.3901999890804291},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.37119999527931213},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.3569999933242798}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6804999709129333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6647999882698059},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.611299991607666},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5440999865531921},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.4880000054836273},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.39959999918937683},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3901999890804291},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.27070000767707825},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.25519999861717224}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tcyb.2020.2990908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2020.2990908","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Cybernetics","raw_type":"journal-article"},{"id":"pmid:32452795","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32452795","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 cybernetics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1252234859","display_name":null,"funder_award_id":"2019C01068","funder_id":"https://openalex.org/F8142712028","funder_display_name":"Key Research and Development Program of Zhejiang Province"},{"id":"https://openalex.org/G151501496","display_name":null,"funder_award_id":"4202058","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G1663456204","display_name":null,"funder_award_id":"4202057","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G2744622150","display_name":"\u5f31\u76d1\u7763\u5b66\u4e60\u6846\u67b6\u4e0b\u5927\u89c4\u6a21\u56fe\u50cf\u8bed\u4e49\u7406\u89e3\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61872032","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3535952189","display_name":null,"funder_award_id":"2019JBM020","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G487022144","display_name":null,"funder_award_id":"9192008","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G7343617800","display_name":null,"funder_award_id":"61673048","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G779815257","display_name":null,"funder_award_id":"4202060","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G8544291730","display_name":null,"funder_award_id":"2020YJS026","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F8142712028","display_name":"Key Research and Development Program of Zhejiang Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W880548201","https://openalex.org/W1142913222","https://openalex.org/W1550508914","https://openalex.org/W1676820704","https://openalex.org/W1974596106","https://openalex.org/W2006158573","https://openalex.org/W2024328138","https://openalex.org/W2106008047","https://openalex.org/W2118585731","https://openalex.org/W2128314196","https://openalex.org/W2137917285","https://openalex.org/W2142863987","https://openalex.org/W2146111596","https://openalex.org/W2172000360","https://openalex.org/W2338068721","https://openalex.org/W2338119550","https://openalex.org/W2358876993","https://openalex.org/W2393384312","https://openalex.org/W2494236530","https://openalex.org/W2528451364","https://openalex.org/W2575032143","https://openalex.org/W2591132901","https://openalex.org/W2592507807","https://openalex.org/W2604553154","https://openalex.org/W2620239873","https://openalex.org/W2733555913","https://openalex.org/W2746791238","https://openalex.org/W2788462285","https://openalex.org/W2807945348","https://openalex.org/W2808448059","https://openalex.org/W2903641274","https://openalex.org/W2962954696","https://openalex.org/W2964666459","https://openalex.org/W2967363906","https://openalex.org/W3014734617","https://openalex.org/W4237969349","https://openalex.org/W4250589301","https://openalex.org/W6676643997","https://openalex.org/W6677739235","https://openalex.org/W6679390333","https://openalex.org/W6679506024","https://openalex.org/W6681102160","https://openalex.org/W6683235360","https://openalex.org/W6695715710","https://openalex.org/W6696851070","https://openalex.org/W6731990857"],"related_works":[],"abstract_inverted_index":{"Partial-label":[0],"learning":[1,76,86,113,134],"(PLL)":[2],"aims":[3],"to":[4,31,115],"solve":[5],"the":[6,24,33,48,62,75,80,89,94,98,112,117,121,124,145,150],"problem":[7,118],"where":[8],"each":[9,41,133],"training":[10,125],"instance":[11],"is":[12,23],"associated":[13],"with":[14,128,140],"a":[15,84,103],"set":[16],"of":[17,21,64,83,123,149],"candidate":[18,34,42,130],"labels,":[19],"one":[20],"which":[22,109],"correct":[25],"label.":[26,50],"Most":[27],"PLL":[28,99,106],"algorithms":[29,53],"try":[30],"disambiguate":[32],"label":[35,43],"set,":[36],"by":[37,79,119],"either":[38],"simply":[39],"treating":[40],"equally":[44],"or":[45],"iteratively":[46],"identifying":[47],"true":[49],"Nonetheless,":[51],"existing":[52],"usually":[54],"treat":[55],"all":[56],"labels":[57,66,131],"and":[58,61,67,101,138,147],"instances":[59,68],"equally,":[60],"complexities":[63],"both":[65],"are":[69],"not":[70],"taken":[71],"into":[72,97],"consideration":[73],"during":[74,132],"stage.":[77],"Inspired":[78],"successful":[81],"application":[82],"self-paced":[85,95,105],"strategy":[87],"in":[88],"machine-learning":[90],"field,":[91],"we":[92],"integrate":[93],"regime":[96],"framework":[100],"propose":[102],"novel":[104],"(SP-PLL)":[107],"algorithm,":[108],"could":[110],"control":[111],"process":[114],"alleviate":[116],"ranking":[120],"priorities":[122],"examples":[126],"together":[127],"their":[129],"iteration.":[135],"Extensive":[136],"experiments":[137],"comparisons":[139],"other":[141],"baseline":[142],"methods":[143],"demonstrate":[144],"effectiveness":[146],"robustness":[148],"proposed":[151],"method.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-05-29T00:00:00"}