{"id":"https://openalex.org/W2013535308","doi":"https://doi.org/10.1109/tcsvt.2013.2276704","title":"Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization","display_name":"Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization","publication_year":2013,"publication_date":"2013-08-29","ids":{"openalex":"https://openalex.org/W2013535308","doi":"https://doi.org/10.1109/tcsvt.2013.2276704","mag":"2013535308"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2013.2276704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2013.2276704","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","raw_type":"journal-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/A5071668416","display_name":"Xiaohua Zhai","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaohua Zhai","raw_affiliation_strings":["Institute of Computer Science and Technology, Peking University, Beijing, China","Institute of Computer Science & Technology, Peking University, Beijing, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Institute of Computer Science & Technology, Peking University, Beijing, China#TAB#","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047811387","display_name":"Yuxin Peng","orcid":"https://orcid.org/0000-0001-7658-3845"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Peng","raw_affiliation_strings":["Institute of Computer Science and Technology, Peking University, Beijing, China","Institute of Computer Science & Technology, Peking University, Beijing, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Institute of Computer Science & Technology, Peking University, Beijing, China#TAB#","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100861201","display_name":"Jianguo Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianguo Xiao","raw_affiliation_strings":["Institute of Computer Science and Technology, Peking University, Beijing, China","Institute of Computer Science & Technology, Peking University, Beijing, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Institute of Computer Science & Technology, Peking University, Beijing, China#TAB#","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071668416"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":1.9351,"has_fulltext":false,"cited_by_count":309,"citation_normalized_percentile":{"value":0.87762087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"24","issue":"6","first_page":"965","last_page":"978"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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":0.9997000098228455,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9994000196456909,"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":0.7413455247879028},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5292346477508545},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4972119629383087},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.49618157744407654},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.48740628361701965},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47599470615386963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4688590168952942},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4259689748287201},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4246172606945038},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37722158432006836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3490244746208191}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7413455247879028},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5292346477508545},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4972119629383087},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.49618157744407654},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.48740628361701965},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47599470615386963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4688590168952942},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4259689748287201},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4246172606945038},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37722158432006836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3490244746208191},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2013.2276704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2013.2276704","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7099999785423279,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G5860771293","display_name":null,"funder_award_id":"61371128","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1554104313","https://openalex.org/W1596649971","https://openalex.org/W1606936348","https://openalex.org/W1994203157","https://openalex.org/W1997231916","https://openalex.org/W2009112943","https://openalex.org/W2009501510","https://openalex.org/W2021122545","https://openalex.org/W2023119292","https://openalex.org/W2025341678","https://openalex.org/W2033673972","https://openalex.org/W2053667957","https://openalex.org/W2073909705","https://openalex.org/W2074309379","https://openalex.org/W2076398700","https://openalex.org/W2105582566","https://openalex.org/W2106277773","https://openalex.org/W2107196683","https://openalex.org/W2108735366","https://openalex.org/W2112037975","https://openalex.org/W2112193096","https://openalex.org/W2114456882","https://openalex.org/W2115115103","https://openalex.org/W2115752676","https://openalex.org/W2123576058","https://openalex.org/W2123590962","https://openalex.org/W2128507168","https://openalex.org/W2137434377","https://openalex.org/W2137918516","https://openalex.org/W2145291599","https://openalex.org/W2147069236","https://openalex.org/W2162867699","https://openalex.org/W2171837816","https://openalex.org/W2241750993","https://openalex.org/W2283195891","https://openalex.org/W4251308012","https://openalex.org/W6633146083","https://openalex.org/W6636274050","https://openalex.org/W6652451885","https://openalex.org/W6677533783"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2905271011","https://openalex.org/W3164948662","https://openalex.org/W4289536128","https://openalex.org/W3153597579","https://openalex.org/W4298151006"],"abstract_inverted_index":{"Cross-media":[0],"retrieval":[1,53,242],"has":[2],"become":[3],"a":[4,32,92,117,145],"key":[5,49],"problem":[6],"in":[7,12,116],"both":[8,179,240],"research":[9],"and":[10,28,78,113,125,183,204,243],"application,":[11],"which":[13,105,169,234],"users":[14],"can":[15,82,164,214],"search":[16],"results":[17],"across":[18],"all":[19],"of":[20,34,71,186,194,201,208,220,239,259],"the":[21,41,48,59,85,111,123,173,176,180,199,206,218,221,227,231,237,257,266],"media":[22,36,46,130,147,159,163,188,196,254],"types":[23,131,189,197,255],"(text,":[24],"image,":[25],"audio,":[26],"video,":[27],"3-D)":[29],"by":[30],"submitting":[31],"query":[33],"any":[35],"type.":[37,148],"How":[38],"to":[39,75,108,172,252],"measure":[40],"content":[42],"similarity":[43],"among":[44],"different":[45,129,158,162,187,195],"is":[47,106,170],"challenge.":[50],"Existing":[51],"cross-media":[52,98,228,241],"methods":[54,141],"usually":[55],"focus":[56,143],"on":[57,144,247],"modeling":[58],"pairwise":[60],"correlation":[61,112,229],"or":[62],"semantic":[63,114],"information":[64,72,115],"separately.":[65],"In":[66,87],"fact,":[67],"these":[68],"two":[69,248],"kinds":[70],"are":[73,190],"complementary":[74],"each":[76,167],"other":[77,177],"optimizing":[79],"them":[80],"simultaneously":[81],"further":[83,235],"improve":[84],"accuracy.":[86],"this":[88],"paper,":[89],"we":[90],"propose":[91],"novel":[93],"feature":[94,139],"learning":[95,103,140],"algorithm":[96],"for":[97,128,157],"data,":[99],"called":[100],"joint":[101,209],"representation":[102,210],"(JRL),":[104],"able":[107],"explore":[109],"jointly":[110],"unified":[118,134],"optimization":[119,135],"framework.":[120],"JRL":[121,152,213],"integrates":[122],"sparse":[124,154],"semisupervised":[126],"regularization":[127],"into":[132,230],"one":[133,150],"problem,":[136],"while":[137],"existing":[138],"generally":[142],"single":[146],"On":[149,175],"hand,":[151,178],"learns":[153],"projection":[155],"matrix":[156],"simultaneously,":[160],"so":[161],"align":[165],"with":[166,250,265],"other,":[168],"robust":[171],"noise.":[174],"labeled":[181],"data":[182,185,203],"unlabeled":[184],"explored.":[191],"Unlabeled":[192],"examples":[193],"increase":[198],"diversity":[200],"training":[202],"boost":[205],"performance":[207,238],"learning.":[211],"Furthermore,":[212],"not":[215],"only":[216],"reduce":[217],"dimension":[219],"original":[222],"features,":[223],"but":[224],"also":[225],"incorporate":[226],"final":[232],"representation,":[233],"improves":[236],"single-media":[244],"retrieval.":[245],"Experiments":[246],"datasets":[249],"up":[251],"five":[253],"show":[256],"effectiveness":[258],"our":[260],"proposed":[261],"approach,":[262],"as":[263],"compared":[264],"state-of-the-art":[267],"methods.":[268]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":36},{"year":2021,"cited_by_count":42},{"year":2020,"cited_by_count":44},{"year":2019,"cited_by_count":49},{"year":2018,"cited_by_count":39},{"year":2017,"cited_by_count":29},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
