{"id":"https://openalex.org/W1892074770","doi":"https://doi.org/10.1109/cvpr.2015.7298755","title":"Semi-supervised low-rank mapping learning for multi-label classification","display_name":"Semi-supervised low-rank mapping learning for multi-label classification","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1892074770","doi":"https://doi.org/10.1109/cvpr.2015.7298755","mag":"1892074770"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","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/A5069749738","display_name":"Liping Jing","orcid":"https://orcid.org/0000-0001-7578-3407"},"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":true,"raw_author_name":"Liping Jing","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University","[Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China]"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"[Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China]","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355692","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-7300-9215"},"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":"Liu Yang","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University","[Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China]"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"[Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China]","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027501725","display_name":"Jian Yu","orcid":"https://orcid.org/0000-0002-9488-5182"},"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":"Jian Yu","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University","[Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China]"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"[Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China]","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010561682","display_name":"Michael K. Ng","orcid":"https://orcid.org/0000-0001-6833-5227"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Michael K. Ng","raw_affiliation_strings":["Department of Mathematics, Hong Kong Baptist Unviersity","Department of Mathematics, Hong Kong Baptist Unviersity, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Hong Kong Baptist Unviersity","institution_ids":["https://openalex.org/I141568987"]},{"raw_affiliation_string":"Department of Mathematics, Hong Kong Baptist Unviersity, Hong Kong","institution_ids":["https://openalex.org/I141568987"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069749738"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":15.1004,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.98942511,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1483","last_page":"1491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9822999835014343,"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":0.9555000066757202,"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/multi-label-classification","display_name":"Multi-label classification","score":0.7273128032684326},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7177549600601196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5899620652198792},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.521565854549408},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4380354881286621},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.41237184405326843},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40775570273399353},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18817153573036194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08086815476417542}],"concepts":[{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.7273128032684326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177549600601196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5899620652198792},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.521565854549408},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4380354881286621},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41237184405326843},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40775570273399353},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18817153573036194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08086815476417542},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7298755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W146125889","https://openalex.org/W167321734","https://openalex.org/W193151672","https://openalex.org/W1736339626","https://openalex.org/W1940008012","https://openalex.org/W1980061733","https://openalex.org/W1997201895","https://openalex.org/W1998839399","https://openalex.org/W2000147660","https://openalex.org/W2032699694","https://openalex.org/W2039958503","https://openalex.org/W2050968963","https://openalex.org/W2065180801","https://openalex.org/W2094651715","https://openalex.org/W2100549954","https://openalex.org/W2101593431","https://openalex.org/W2103972604","https://openalex.org/W2104290444","https://openalex.org/W2108119513","https://openalex.org/W2109317801","https://openalex.org/W2114315281","https://openalex.org/W2114393161","https://openalex.org/W2115119296","https://openalex.org/W2122090912","https://openalex.org/W2135733005","https://openalex.org/W2138290126","https://openalex.org/W2152322845","https://openalex.org/W2154455818","https://openalex.org/W2155144632","https://openalex.org/W2161824996","https://openalex.org/W2165238043","https://openalex.org/W2170487914","https://openalex.org/W2176228818","https://openalex.org/W2292376821","https://openalex.org/W2951085447","https://openalex.org/W2951238624","https://openalex.org/W2997701990","https://openalex.org/W4285719527","https://openalex.org/W6606838427","https://openalex.org/W6607898275","https://openalex.org/W6640485552","https://openalex.org/W6649956883","https://openalex.org/W6675164516","https://openalex.org/W6675529100","https://openalex.org/W6675747103","https://openalex.org/W6677150689","https://openalex.org/W6677671969","https://openalex.org/W6680051633","https://openalex.org/W6682494755","https://openalex.org/W6682939927","https://openalex.org/W6684100978","https://openalex.org/W6685347822","https://openalex.org/W6697129201","https://openalex.org/W6764172607","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2152992791","https://openalex.org/W4281776617","https://openalex.org/W4285328440","https://openalex.org/W4390062853","https://openalex.org/W4389256085","https://openalex.org/W4313644201","https://openalex.org/W1983574915","https://openalex.org/W2296406158","https://openalex.org/W2149795293","https://openalex.org/W2086141029"],"abstract_inverted_index":{"Multi-label":[0],"problems":[1],"arise":[2],"in":[3,16],"various":[4],"domains":[5],"including":[6],"automatic":[7],"multimedia":[8,143],"data":[9,45,107],"categorization,":[10],"and":[11,19,37,130,155,158],"have":[12],"generated":[13],"significant":[14],"interest":[15],"computer":[17],"vision":[18],"machine":[20],"learning":[21],"community.":[22],"However,":[23],"existing":[24],"methods":[25],"do":[26],"not":[27],"adequately":[28],"address":[29],"two":[30,64],"key":[31],"challenges:":[32],"exploiting":[33],"correlations":[34,154],"between":[35],"labels":[36],"making":[38],"up":[39],"for":[40],"the":[41,71,80,92,104,110,147,152],"lack":[42],"of":[43,70,128],"labeled":[44,106],"or":[46],"even":[47],"missing":[48],"labels.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53,114],"proposed":[54,148],"a":[55,99],"semi-supervised":[56],"low-rank":[57],"mapping":[58,76,89],"(SLRM)":[59],"model":[60,67,122],"to":[61,77,90,102,119],"handle":[62],"these":[63],"challenges.":[65],"SLRM":[66,121],"takes":[68],"advantage":[69],"nuclear":[72],"norm":[73],"regularization":[74],"on":[75,88,124,140],"effectively":[78],"capture":[79,91],"label":[81,153,160],"correlations.":[82],"Meanwhile,":[83],"it":[84,132],"introduces":[85],"manifold":[86],"regularizer":[87],"intrinsic":[93],"structure":[94],"among":[95],"data,":[96],"which":[97],"provides":[98],"good":[100],"way":[101],"reduce":[103],"required":[105],"with":[108,136],"improving":[109],"classification":[111],"performance.":[112],"Furthermore,":[113],"designed":[115],"an":[116],"efficient":[117],"algorithm":[118],"solve":[120],"based":[123],"alternating":[125],"direction":[126],"method":[127,149],"multipliers":[129],"thus":[131],"can":[133,150],"efficiently":[134],"deal":[135],"large-scale":[137],"datasets.":[138],"Experiments":[139],"four":[141],"real-world":[142],"datasets":[144],"demonstrate":[145],"that":[146],"exploit":[151],"obtain":[156],"promising":[157],"better":[159],"prediction":[161],"results":[162],"than":[163],"state-of-the-art":[164],"methods.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}