{"id":"https://openalex.org/W2903217596","doi":"https://doi.org/10.1109/icpr.2018.8545510","title":"Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding","display_name":"Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903217596","doi":"https://doi.org/10.1109/icpr.2018.8545510","mag":"2903217596"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5058268359","display_name":"Hao Zhu","orcid":"https://orcid.org/0000-0003-4411-0933"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hao Zhu","raw_affiliation_strings":["Vector Lab JD Finance, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Vector Lab JD Finance, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081531776","display_name":"Mingxue Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingxue Xia","raw_affiliation_strings":["Vector Lab JD Finance, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Vector Lab JD Finance, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058268359"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1629,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60582451,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"11","issue":null,"first_page":"1331","last_page":"1336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9846000075340271,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9846000075340271,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9828000068664551,"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/T10057","display_name":"Face and Expression Recognition","score":0.9776999950408936,"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/computer-science","display_name":"Computer science","score":0.7500925064086914},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6777754426002502},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6110129952430725},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5854108929634094},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5729736089706421},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5143173933029175},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45325005054473877},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4478939473628998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44050782918930054},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4301438629627228},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.42388346791267395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37275269627571106}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7500925064086914},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6777754426002502},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6110129952430725},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5854108929634094},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5729736089706421},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5143173933029175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45325005054473877},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4478939473628998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44050782918930054},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4301438629627228},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42388346791267395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37275269627571106},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.46000000834465027,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1500351990","https://openalex.org/W1992837555","https://openalex.org/W2005876975","https://openalex.org/W2063978378","https://openalex.org/W2097018403","https://openalex.org/W2098012923","https://openalex.org/W2104290444","https://openalex.org/W2107008379","https://openalex.org/W2109405055","https://openalex.org/W2112447569","https://openalex.org/W2113606819","https://openalex.org/W2117302351","https://openalex.org/W2122457239","https://openalex.org/W2122992239","https://openalex.org/W2132146097","https://openalex.org/W2135046866","https://openalex.org/W2139823104","https://openalex.org/W2154455818","https://openalex.org/W2154560360","https://openalex.org/W2997701990","https://openalex.org/W3105543546","https://openalex.org/W3123825806","https://openalex.org/W6629858893","https://openalex.org/W6675747103","https://openalex.org/W6676132248","https://openalex.org/W6676727762","https://openalex.org/W6676903177","https://openalex.org/W6677528152","https://openalex.org/W6678315064","https://openalex.org/W6678569274","https://openalex.org/W6679392215","https://openalex.org/W6680434193","https://openalex.org/W6682494755","https://openalex.org/W6786091634"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1,7,169],"(SSL)":[2],"generalizes":[3],"and":[4,11,126,136,184,249],"improves":[5],"supervised":[6],"using":[8,160,167],"labeled":[9],"data":[10,28,38,185],"unlabeled":[12,37],"data.":[13,59],"With":[14],"the":[15,19,24,30,44,58,72,86,117,129,163,180,190,200,229,233],"rapid":[16],"development":[17],"of":[18,27,36,57,85],"Internet":[20],"as":[21,23,214,216],"well":[22,215],"increasing":[25],"availability":[26],"in":[29,47,83,116,132,175,199,219],"open":[31],"web,":[32],"collecting":[33],"tremendous":[34],"amount":[35],"has":[39],"became":[40],"more":[41,134,149,238],"feasible.":[42],"As":[43],"central":[45],"notion":[46],"SSL,":[48,88],"smoothness":[49],"is":[50,90,152,186,194,246],"often":[51],"defined":[52],"on":[53,109],"a":[54,106,133,148],"graph":[55,107,119],"representation":[56,143],"However,":[60],"only":[61,179],"few":[62],"researches":[63],"up":[64],"to":[65,92,104,122,127,223,252],"date":[66],"adapt":[67],"graph-based":[68],"SSL":[69,212,236,256],"approaches":[70,79],"into":[71,188],"large":[73,210,234,254],"scale":[74,211,235,255],"Solution.":[75],"Even":[76],"if":[77],"approximation":[78],"are":[80,112,165],"commonly":[81],"used":[82],"methods":[84,213],"large-scale":[87],"it":[89],"difficult":[91],"outperform":[93],"original":[94],"methods.":[95,242],"In":[96],"this":[97],"paper,":[98],"we":[99,203],"propose":[100],"an":[101,247],"efficient":[102,135,239,248],"method":[103,207,231,245],"construct":[105],"based":[108],"anchors.":[110],"There":[111],"two":[113],"major":[114],"concerns":[115],"anchor":[118,146],"generally:":[120],"how":[121],"learn":[123],"better":[124],"anchors":[125,164,183,193],"present":[128],"input":[130],"both":[131],"effective":[137,250],"fashion.":[138],"Intuitively,":[139],"compared":[140],"with":[141,155,170],"sparse":[142],"(e.g.":[144],"local":[145],"embedding),":[147],"straightforward":[150],"approach":[151],"marginal":[153],"regression":[154],"non-negative":[156],"constraint.":[157],"Rather":[158],"than":[159,240],"clustering":[161],"algorithms,":[162],"trained":[166],"dictionary":[168],"sparsity":[171],"constraints.":[172],"And":[173],"thus":[174],"our":[176,206,244],"approach,":[177],"not":[178],"relation":[181,191],"between":[182,192],"taken":[187],"consideration,":[189],"also":[195],"regarded.":[196],"Beyond":[197],"that,":[198],"evaluation":[201],"section,":[202],"demonstrate":[204],"that":[205],"outperforms":[208],"other":[209],"traditional":[217],"ones":[218],"classification":[220],"performance":[221],"according":[222],"several":[224],"classical":[225],"datasets.":[226],"Further":[227],"more,":[228],"proposed":[230],"solves":[232],"problem":[237],"current":[241],"Therefore,":[243],"alternative":[251],"handle":[253],"problem.":[257]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}