{"id":"https://openalex.org/W2589551803","doi":"https://doi.org/10.1109/icmlc.2016.7860877","title":"Trace norm regularized canonical correlation analysis","display_name":"Trace norm regularized canonical correlation analysis","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2589551803","doi":"https://doi.org/10.1109/icmlc.2016.7860877","mag":"2589551803"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2016.7860877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2016.7860877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Machine Learning and Cybernetics (ICMLC)","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/A5067065399","display_name":"Hongqi Li","orcid":"https://orcid.org/0000-0003-4898-4857"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hong-Qi Li","raw_affiliation_strings":["Beijing Key Lab of Petroleum Data Mining, China University of Petroleum(Beijing), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Petroleum Data Mining, China University of Petroleum(Beijing), Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022619051","display_name":"Yuan-Yuan Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan-Yuan Lu","raw_affiliation_strings":["Beijing Key Lab of Petroleum Data Mining, China University of Petroleum(Beijing), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Petroleum Data Mining, China University of Petroleum(Beijing), Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101878084","display_name":"Xin Shu","orcid":"https://orcid.org/0000-0001-6126-4366"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Shu","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100767641","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0002-0502-8796"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["Beijing Key Lab of Petroleum Data Mining, China University of Petroleum(Beijing), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Petroleum Data Mining, China University of Petroleum(Beijing), Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067065399"],"corresponding_institution_ids":["https://openalex.org/I204553293"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17965328,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"54","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"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":0.9994999766349792,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.8331176042556763},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.6288577914237976},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5615898370742798},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5430142879486084},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.5314447283744812},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5259851813316345},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.49003589153289795},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.48506155610084534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4575764834880829},{"id":"https://openalex.org/keywords/transformation-matrix","display_name":"Transformation matrix","score":0.4492838382720947},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.43455877900123596},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4287341237068176},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4111676812171936},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39109453558921814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26743900775909424},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18799430131912231},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.10463684797286987}],"concepts":[{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.8331176042556763},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.6288577914237976},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5615898370742798},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5430142879486084},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5314447283744812},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5259851813316345},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.49003589153289795},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.48506155610084534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4575764834880829},{"id":"https://openalex.org/C165443888","wikidata":"https://www.wikidata.org/wiki/Q1482183","display_name":"Transformation matrix","level":3,"score":0.4492838382720947},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.43455877900123596},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4287341237068176},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4111676812171936},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39109453558921814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26743900775909424},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18799430131912231},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.10463684797286987},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2016.7860877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2016.7860877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W14566539","https://openalex.org/W1534408902","https://openalex.org/W1965337535","https://openalex.org/W1966096622","https://openalex.org/W1984983329","https://openalex.org/W1998635907","https://openalex.org/W1999085008","https://openalex.org/W2008929650","https://openalex.org/W2025341678","https://openalex.org/W2035205472","https://openalex.org/W2036010272","https://openalex.org/W2042759724","https://openalex.org/W2047631354","https://openalex.org/W2053186076","https://openalex.org/W2065180801","https://openalex.org/W2070127246","https://openalex.org/W2076455317","https://openalex.org/W2083095455","https://openalex.org/W2098290597","https://openalex.org/W2100235303","https://openalex.org/W2103250033","https://openalex.org/W2103972604","https://openalex.org/W2109521352","https://openalex.org/W2122090912","https://openalex.org/W2122825543","https://openalex.org/W2125290066","https://openalex.org/W2129812935","https://openalex.org/W2135346934","https://openalex.org/W2138768526","https://openalex.org/W2145962650","https://openalex.org/W2151211319","https://openalex.org/W2152758153","https://openalex.org/W2154872931","https://openalex.org/W2171033594","https://openalex.org/W2536620281","https://openalex.org/W2951443864","https://openalex.org/W2994340921","https://openalex.org/W6600544787","https://openalex.org/W6635552349","https://openalex.org/W6640723650","https://openalex.org/W6675955514","https://openalex.org/W6677671969","https://openalex.org/W6682644385"],"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/W4296209631","https://openalex.org/W3134705486","https://openalex.org/W2121524531","https://openalex.org/W2619932150","https://openalex.org/W2059785080"],"abstract_inverted_index":{"Canonical":[0],"correlation":[1,12],"analysis(CCA)":[2],"is":[3],"a":[4,91],"popular":[5],"technique":[6],"that":[7,64],"works":[8],"for":[9],"finding":[10],"the":[11,21,43,68,77,87,96,108],"between":[13,80],"two":[14],"sets":[15,106],"of":[16,23,70,90,99,110],"variables.":[17],"However,":[18],"CCA":[19],"faces":[20],"problem":[22,69],"small":[24,71],"sample":[25,72],"size":[26,73],"in":[27],"dealing":[28],"with":[29],"high":[30],"dimensional":[31],"data.":[32],"Several":[33],"approaches":[34],"have":[35],"been":[36],"proposed":[37],"to":[38,48,95],"overcome":[39],"this":[40,56],"issue,":[41],"but":[42,74],"resulting":[44],"transformation":[45,92],"matrix":[46,93],"fails":[47],"extract":[49],"shared":[50],"structures":[51,79],"among":[52],"data":[53,105],"samples.":[54],"In":[55],"paper,":[57],"we":[58],"propose":[59],"trace":[60,100],"norm":[61],"regularized":[62],"CCA(SRCCA)":[63],"not":[65],"only":[66],"tackles":[67],"also":[75],"uncover":[76],"underlying":[78],"target":[81],"classes.":[82],"Specifically,":[83],"our":[84,111],"formulation":[85],"characterizes":[86],"intrinsic":[88],"dimensionality":[89],"owing":[94],"appealing":[97],"property":[98],"norm.":[101],"Evaluations":[102],"over":[103],"public":[104],"deliver":[107],"effectiveness":[109],"algorithm.":[112]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
