{"id":"https://openalex.org/W2977824741","doi":"https://doi.org/10.1109/ijcnn.2019.8852123","title":"Deep Multi-view Learning from Sequential Data without Correspondence","display_name":"Deep Multi-view Learning from Sequential Data without Correspondence","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977824741","doi":"https://doi.org/10.1109/ijcnn.2019.8852123","mag":"2977824741"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5077447448","display_name":"Tung Doan","orcid":null},"institutions":[{"id":"https://openalex.org/I200475212","display_name":"The Graduate University for Advanced Studies, SOKENDAI","ror":"https://ror.org/0516ah480","country_code":"JP","type":"education","lineage":["https://openalex.org/I200475212"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tung Doan","raw_affiliation_strings":["SOKENDAI, The Graduate University for Advanced Studies, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"SOKENDAI, The Graduate University for Advanced Studies, Kanagawa, Japan","institution_ids":["https://openalex.org/I200475212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087434029","display_name":"Atsuhiro Takasu","orcid":"https://orcid.org/0000-0002-9061-7949"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takasu Atsuhiro","raw_affiliation_strings":["National Institute of Informatics, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Informatics, Tokyo, Japan","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5077447448"],"corresponding_institution_ids":["https://openalex.org/I200475212"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54279279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"91","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10812","display_name":"Human Pose and Action Recognition","score":0.9993000030517578,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7883647680282593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6885541081428528},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.655961275100708},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.588981032371521},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5638991594314575},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.52309250831604},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5197217464447021},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.5170753598213196},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4392431974411011},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36176609992980957},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1331840455532074}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7883647680282593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6885541081428528},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.655961275100708},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.588981032371521},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5638991594314575},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.52309250831604},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5197217464447021},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.5170753598213196},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4392431974411011},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36176609992980957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1331840455532074},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W64698994","https://openalex.org/W1523385540","https://openalex.org/W1560013842","https://openalex.org/W1836465849","https://openalex.org/W1880262756","https://openalex.org/W1883346539","https://openalex.org/W1953764758","https://openalex.org/W1978394996","https://openalex.org/W2025341678","https://openalex.org/W2112796928","https://openalex.org/W2121950477","https://openalex.org/W2124958607","https://openalex.org/W2134731454","https://openalex.org/W2150696241","https://openalex.org/W2151831732","https://openalex.org/W2153635508","https://openalex.org/W2165874743","https://openalex.org/W2169658215","https://openalex.org/W2184188583","https://openalex.org/W2187089797","https://openalex.org/W2406249259","https://openalex.org/W2408716783","https://openalex.org/W2554185348","https://openalex.org/W2595142274","https://openalex.org/W2623638694","https://openalex.org/W2962914230","https://openalex.org/W2963405869","https://openalex.org/W3028642772","https://openalex.org/W4231510805","https://openalex.org/W4237723258","https://openalex.org/W6602657573","https://openalex.org/W6631216910","https://openalex.org/W6638667902","https://openalex.org/W6639103823","https://openalex.org/W6639619044","https://openalex.org/W6682511423","https://openalex.org/W6684578312","https://openalex.org/W6686207219","https://openalex.org/W6713821351","https://openalex.org/W6714032056","https://openalex.org/W6734312481","https://openalex.org/W6734797129","https://openalex.org/W6777926273"],"related_works":["https://openalex.org/W1565185441","https://openalex.org/W1968846550","https://openalex.org/W302711736","https://openalex.org/W2313359725","https://openalex.org/W2375550484","https://openalex.org/W2619932150","https://openalex.org/W3134705486","https://openalex.org/W2013124714","https://openalex.org/W2121524531","https://openalex.org/W2752621239"],"abstract_inverted_index":{"Multi-view":[0],"representation":[1],"learning":[2,11,99,118],"has":[3],"become":[4],"an":[5],"active":[6],"research":[7],"topic":[8],"in":[9,32,38,57,65,70],"machine":[10],"and":[12,34,48,67],"data":[13,25,51],"mining.":[14],"One":[15],"underlying":[16],"assumption":[17],"of":[18,26,60,73,134,140,151],"the":[19,27,53,58,71,100,127,131,141,148],"conventional":[20],"methods":[21,77],"is":[22],"that":[23,62,92,106],"training":[24],"views":[28,54],"must":[29],"be":[30,104],"equal":[31],"size":[33],"sample-wise":[35],"matching.":[36],"However,":[37],"many":[39],"real-world":[40],"applications,":[41],"such":[42,79],"as":[43],"video":[44],"analysis,":[45],"text":[46],"streaming,":[47],"signal":[49],"processing,":[50],"for":[52],"often":[55],"come":[56],"form":[59],"sequences,":[61],"are":[63],"different":[64],"length":[66],"misaligned,":[68],"resulting":[69],"failure":[72],"directly":[74],"applying":[75],"existing":[76],"to":[78],"problems.":[80],"In":[81],"this":[82],"paper,":[83],"we":[84],"first":[85],"introduce":[86],"a":[87,115,137],"novel":[88],"deep":[89,110],"multi-view":[90,117],"model":[91,124],"can":[93,103],"implicitly":[94],"discover":[95],"sample":[96],"correspondence":[97],"while":[98],"representation.":[101],"It":[102],"shown":[105],"our":[107,123,152],"method":[108],"generalizes":[109],"canonical":[111],"correlation":[112],"analysis":[113],"-":[114],"popular":[116],"method.":[119],"We":[120],"then":[121],"extend":[122],"by":[125],"integrating":[126],"objective":[128],"function":[129],"with":[130],"reconstruction":[132],"losses":[133],"autoencoders,":[135],"forming":[136],"new":[138],"variant":[139],"proposed":[142],"model.":[143],"Extensively":[144],"experimental":[145],"results":[146],"demonstrate":[147],"superior":[149],"performances":[150],"models":[153],"over":[154],"competing":[155],"methods.":[156]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
