{"id":"https://openalex.org/W2912741785","doi":"https://doi.org/10.1109/access.2019.2895363","title":"A New Robust Deep Canonical Correlation Analysis Algorithm for Small Sample Problems","display_name":"A New Robust Deep Canonical Correlation Analysis Algorithm for Small Sample Problems","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2912741785","doi":"https://doi.org/10.1109/access.2019.2895363","mag":"2912741785"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2895363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2895363","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08631009.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08631009.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047530483","display_name":"Yan Liu","orcid":"https://orcid.org/0000-0002-5331-3655"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Liu","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5331-3655","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101793983","display_name":"Yun Li","orcid":"https://orcid.org/0000-0002-7628-0358"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Li","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055757510","display_name":"Yunhao Yuan","orcid":"https://orcid.org/0000-0003-3712-443X"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun-Hao Yuan","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100681671","display_name":"Hui Zhang","orcid":"https://orcid.org/0000-0003-4868-9057"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhang","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047530483"],"corresponding_institution_ids":["https://openalex.org/I78978612"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.3062,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.58662255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"33631","last_page":"33639"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9954000115394592,"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/T10057","display_name":"Face and Expression Recognition","score":0.9954000115394592,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7708573341369629},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.70406574010849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6920740604400635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571642160415649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6415644884109497},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6199589967727661},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5885372161865234},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5483017563819885},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.508683979511261},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.470429390668869},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45719093084335327},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4510461986064911},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43508633971214294},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21018344163894653},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07265815138816833}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7708573341369629},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.70406574010849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6920740604400635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571642160415649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6415644884109497},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6199589967727661},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5885372161865234},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5483017563819885},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.508683979511261},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.470429390668869},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45719093084335327},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4510461986064911},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43508633971214294},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21018344163894653},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07265815138816833},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2895363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2895363","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08631009.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:97d58517530a4342b7cda97c76c93c58","is_oa":true,"landing_page_url":"https://doaj.org/article/97d58517530a4342b7cda97c76c93c58","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 33631-33639 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2895363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2895363","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08631009.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G2062066366","display_name":null,"funder_award_id":"61402203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3085871894","display_name":null,"funder_award_id":"61611540347","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G325009246","display_name":null,"funder_award_id":"61703362","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3714500080","display_name":null,"funder_award_id":"2017CXJ033","funder_id":"https://openalex.org/F4320324130","funder_display_name":"Yangzhou University"},{"id":"https://openalex.org/G5439587901","display_name":null,"funder_award_id":"61472344","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/F4320324130","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2912741785.pdf","grobid_xml":"https://content.openalex.org/works/W2912741785.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W87822204","https://openalex.org/W1523385540","https://openalex.org/W1531883353","https://openalex.org/W1536675765","https://openalex.org/W1601511468","https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1883346539","https://openalex.org/W1949478088","https://openalex.org/W1984983329","https://openalex.org/W2011390239","https://openalex.org/W2025341678","https://openalex.org/W2042058698","https://openalex.org/W2045907009","https://openalex.org/W2049365101","https://openalex.org/W2053186076","https://openalex.org/W2060794592","https://openalex.org/W2072942628","https://openalex.org/W2082925319","https://openalex.org/W2089468765","https://openalex.org/W2100495367","https://openalex.org/W2103560185","https://openalex.org/W2108502868","https://openalex.org/W2112796928","https://openalex.org/W2113957760","https://openalex.org/W2117287331","https://openalex.org/W2121647436","https://openalex.org/W2130055251","https://openalex.org/W2132549764","https://openalex.org/W2135346934","https://openalex.org/W2142674578","https://openalex.org/W2145962650","https://openalex.org/W2153635508","https://openalex.org/W2154872931","https://openalex.org/W2157297238","https://openalex.org/W2163605009","https://openalex.org/W2165673880","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2285907740","https://openalex.org/W2295584157","https://openalex.org/W2607333215","https://openalex.org/W2623575224","https://openalex.org/W2766637189","https://openalex.org/W2782794990","https://openalex.org/W2783674487","https://openalex.org/W2997574889","https://openalex.org/W6631216910","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6639103823","https://openalex.org/W6681096077","https://openalex.org/W6682644385","https://openalex.org/W6684191040","https://openalex.org/W6739392561","https://openalex.org/W6758696646"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2315242963","https://openalex.org/W2144490279","https://openalex.org/W4241742979"],"abstract_inverted_index":{"As":[0],"a":[1,13],"nonlinear":[2],"feature":[3,118],"learning":[4],"method,":[5],"deep":[6,23],"canonical":[7],"correlation":[8],"analysis":[9],"(DCCA)":[10],"has":[11,164],"got":[12],"great":[14,182],"success":[15],"in":[16,48,124,184],"computer":[17],"vision.":[18],"Compared":[19],"with":[20],"kernel":[21],"methods,":[22,138],"neural":[24,126,141,147],"networks":[25,148],"can":[26],"more":[27],"easily":[28],"process":[29],"large":[30],"amounts":[31],"of":[32,61,101,115],"training":[33,42,62,87],"data":[34],"and":[35,57,65,93,121,145,158,179],"do":[36],"not":[37],"require":[38],"referring":[39],"to":[40,53,97,111],"the":[41,49,54,58,78,83,99],"set":[43],"at":[44],"test":[45],"time.":[46],"However,":[47],"real":[50],"world,":[51],"due":[52],"noise":[55],"disturbance":[56],"limited":[59],"number":[60],"samples,":[63],"within-set":[64,92],"between-set":[66,94],"sample":[67,102],"covariance":[68,103],"matrices":[69,96,104],"cannot":[70],"usually":[71],"be":[72],"estimated":[73],"accurately,":[74],"which":[75],"causes":[76],"that":[77,162,177],"gradient":[79,106,128],"direction":[80,107],"deviates":[81],"from":[82],"true":[84],"one":[85],"when":[86],"DCCA.":[88],"It":[89],"incorporates":[90],"fractional-order":[91],"scatter":[95],"reduce":[98],"deviations":[100],"for":[105],"correction.":[108],"In":[109],"addition,":[110],"make":[112],"full":[113],"use":[114],"convolutional":[116,135,140],"network's":[117],"extraction":[119],"ability":[120],"fractional":[122],"model":[123],"modifying":[125],"network":[127],"directions,":[129],"we":[130],"further":[131],"propose":[132],"two":[133],"novel":[134],"network-based":[136,142],"FDCCA":[137,143,150,163],"named":[139],"(CNN-FDCCA)":[144],"two-convolutional":[146],"based":[149],"(2CNNs-FDCCA),":[151],"respectively.":[152],"The":[153,171],"experimental":[154],"results":[155],"on":[156,173],"MNIST":[157],"RAVDNESS":[159],"datasets":[160],"show":[161,176],"better":[165],"recognition":[166],"rates":[167],"than":[168],"existing":[169],"methods.":[170],"experiments":[172],"AT&T":[174],"dataset":[175],"CNN-FDCCA":[178],"2CNNs-FDCCA":[180],"have":[181],"robustness":[183],"processing":[185],"images.":[186]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
