{"id":"https://openalex.org/W2904398352","doi":"https://doi.org/10.1609/aaai.v33i01.33015016","title":"Partial Multi-Label Learning by Low-Rank and Sparse Decomposition","display_name":"Partial Multi-Label Learning by Low-Rank and Sparse Decomposition","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2904398352","doi":"https://doi.org/10.1609/aaai.v33i01.33015016","mag":"2904398352"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33015016","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33015016","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v33i01.33015016","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046146572","display_name":"Lijuan Sun","orcid":"https://orcid.org/0000-0003-2873-9803"},"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":"Lijuan Sun","raw_affiliation_strings":["Beijing Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085406955","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 Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100453676","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 Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023442981","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 Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033896100","display_name":"Yi Jin","orcid":"https://orcid.org/0000-0001-8408-3816"},"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":"Yi Jin","raw_affiliation_strings":["Beijing JiaoTong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing JiaoTong University","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":null,"has_fulltext":true,"cited_by_count":124,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":"01","first_page":"5016","last_page":"5023"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9768000245094299,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9767000079154968,"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/overfitting","display_name":"Overfitting","score":0.740534782409668},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6224651336669922},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.5939812064170837},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.49208539724349976},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.4847680926322937},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.47818973660469055},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.47029444575309753},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44364115595817566},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4351918399333954},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4345684051513672},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40070056915283203},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3574926257133484},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3340299725532532},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2504008710384369},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08359462022781372},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.08200061321258545}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.740534782409668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6224651336669922},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5939812064170837},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.49208539724349976},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.4847680926322937},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.47818973660469055},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.47029444575309753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44364115595817566},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4351918399333954},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4345684051513672},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40070056915283203},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3574926257133484},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3340299725532532},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2504008710384369},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08359462022781372},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.08200061321258545},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v33i01.33015016","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33015016","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/4433","is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/4433","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4433/4311","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33015016","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33015016","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G130032563","display_name":null,"funder_award_id":"61872035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"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/G3102177199","display_name":null,"funder_award_id":"2017JBZ108","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G619412513","display_name":null,"funder_award_id":"61872035","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6470528734","display_name":"\u6d77\u91cf\u793e\u7fa4\u56fe\u50cf\u8bed\u4e49\u7406\u89e3\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61472028","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"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/G7353936062","display_name":null,"funder_award_id":"61502026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G766001132","display_name":null,"funder_award_id":"2017JBZ108","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321106","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W79405465","https://openalex.org/W1565746575","https://openalex.org/W1592096085","https://openalex.org/W2019899889","https://openalex.org/W2025335430","https://openalex.org/W2042759724","https://openalex.org/W2052684427","https://openalex.org/W2078120539","https://openalex.org/W2090630554","https://openalex.org/W2100549954","https://openalex.org/W2103972604","https://openalex.org/W2114315281","https://openalex.org/W2118120419","https://openalex.org/W2118712128","https://openalex.org/W2120387782","https://openalex.org/W2129144539","https://openalex.org/W2132509897","https://openalex.org/W2135733005","https://openalex.org/W2138290126","https://openalex.org/W2140335411","https://openalex.org/W2145241906","https://openalex.org/W2157888812","https://openalex.org/W2160569988","https://openalex.org/W2166912588","https://openalex.org/W2205224283","https://openalex.org/W2285223868","https://openalex.org/W2359108789","https://openalex.org/W2733555913","https://openalex.org/W2745660053","https://openalex.org/W2788462285","https://openalex.org/W2788764852","https://openalex.org/W2951085447","https://openalex.org/W2997519153","https://openalex.org/W4230451635","https://openalex.org/W6656842176","https://openalex.org/W6677222910","https://openalex.org/W6681153989","https://openalex.org/W6681347208","https://openalex.org/W6683097776","https://openalex.org/W6683235360","https://openalex.org/W6712783742","https://openalex.org/W6748505758"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3103289951","https://openalex.org/W4294691692","https://openalex.org/W2789229307","https://openalex.org/W2898722594","https://openalex.org/W2125326641","https://openalex.org/W2557895864","https://openalex.org/W133838137"],"abstract_inverted_index":{"Multi-Label":[0],"Learning":[1,70,118],"(MLL)":[2],"aims":[3],"to":[4,34,75,95,157,166,176,188,192,220],"learn":[5],"from":[6,86,197],"the":[7,36,46,82,83,87,98,106,130,153,162,172,178,184,194,203,208,212,222],"training":[8],"data":[9,51],"where":[10,152,211],"each":[11],"example":[12],"is":[13,52,73,155,164,218],"represented":[14],"by":[15,61,119],"a":[16,22,66,114,135,143],"single":[17],"instance":[18],"while":[19],"associated":[20],"with":[21,77],"set":[23,133],"of":[24,38,101],"candidate":[25],"labels.":[26,40],"Most":[27],"existing":[28],"MLL":[29,63],"methods":[30],"are":[31],"typically":[32],"designed":[33],"handle":[35],"problem":[37],"missing":[39],"However,":[41],"in":[42,93],"many":[43],"real-world":[44],"scenarios,":[45],"labeling":[47],"information":[48],"for":[49],"multi-label":[50,88],"always":[53],"redundant":[54],",":[55],"which":[56],"can":[57,231],"not":[58],"be":[59,158,167,189],"solved":[60],"classical":[62],"methods,":[64],"thus":[65],"novel":[67,115],"Partial":[68,116],"Multi-label":[69,117],"(PML)":[71],"framework":[72],"proposed":[74,195],"cope":[76],"such":[78],"problem,":[79],"i.e.":[80],"removing":[81],"noisy":[84],"labels":[85,205],"sets.":[89],"In":[90],"this":[91],"paper,":[92],"order":[94],"further":[96],"improve":[97],"denoising":[99],"capability":[100],"PML":[102],"framework,":[103],"we":[104,127,170,201],"utilize":[105,171],"low-rank":[107],"and":[108,112,121,138,147,161,181],"sparse":[109],"decomposition":[110,123],"scheme":[111],"propose":[113],"Low-Rank":[120],"Sparse":[122],"(PML-LRS)":[124],"approach.":[125],"Specifically,":[126],"first":[128],"reformulate":[129],"observed":[131],"label":[132,136,145,150,179,209],"into":[134,142],"matrix,":[137,151],"then":[139],"decompose":[140],"it":[141],"groundtruth":[144],"matrix":[146,175,187],"an":[148],"irrelevant":[149],"former":[154],"constrained":[156],"low":[159,190],"rank":[160,191],"latter":[163],"assumed":[165],"sparse.":[168],"Next,":[169],"feature":[173,185],"mapping":[174,186],"explore":[177],"correlations":[180],"meanwhile":[182],"constrain":[183],"prevent":[193],"method":[196],"being":[198],"overfitting.":[199],"Finally,":[200],"obtain":[202],"ground-truth":[204],"via":[206],"minimizing":[207],"loss,":[210],"Augmented":[213],"Lagrange":[214],"Multiplier":[215],"(ALM)":[216],"algorithm":[217],"incorporated":[219],"solve":[221],"optimization":[223],"problem.":[224],"Enormous":[225],"experimental":[226],"results":[227],"demonstrate":[228],"that":[229],"PML-LRS":[230],"achieve":[232],"superior":[233],"or":[234],"competitive":[235],"performance":[236],"against":[237],"other":[238],"state-of-the-art":[239],"methods.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
