{"id":"https://openalex.org/W2964931725","doi":"https://doi.org/10.1145/3332183","title":"Maximally Correlated Principal Component Analysis Based on Deep Parameterization Learning","display_name":"Maximally Correlated Principal Component Analysis Based on Deep Parameterization Learning","publication_year":2019,"publication_date":"2019-07-29","ids":{"openalex":"https://openalex.org/W2964931725","doi":"https://doi.org/10.1145/3332183","mag":"2964931725"},"language":"en","primary_location":{"id":"doi:10.1145/3332183","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3332183","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","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/A5100353673","display_name":"Hao Chen","orcid":"https://orcid.org/0009-0001-6480-7976"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Chen","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101612297","display_name":"Jinghua Li","orcid":"https://orcid.org/0000-0002-5583-8260"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghua Li","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015817857","display_name":"Junbin Gao","orcid":"https://orcid.org/0000-0001-9803-0256"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Junbin Gao","raw_affiliation_strings":["The University of Sydney, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082380547","display_name":"Yanfeng Sun","orcid":"https://orcid.org/0000-0002-0872-384X"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanfeng Sun","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027329007","display_name":"Yongli Hu","orcid":"https://orcid.org/0000-0003-0440-438X"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongli Hu","raw_affiliation_strings":["Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020527092","display_name":"Baocai Yin","orcid":"https://orcid.org/0000-0003-3121-1823"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baocai Yin","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3051,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.60399641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"13","issue":"4","first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9984999895095825,"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":0.9984999895095825,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9757000207901001,"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/principal-component-analysis","display_name":"Principal component analysis","score":0.8607653379440308},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.8581244945526123},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6783038377761841},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6007298827171326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5687746405601501},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5384118556976318},{"id":"https://openalex.org/keywords/sparse-pca","display_name":"Sparse PCA","score":0.5352557301521301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4937301576137543},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.48407426476478577},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4697941541671753},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.46967941522598267},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4516248106956482},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4399033784866333},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41027671098709106},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3661883473396301},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36213192343711853},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0902671217918396}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8607653379440308},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.8581244945526123},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6783038377761841},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6007298827171326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687746405601501},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5384118556976318},{"id":"https://openalex.org/C24252448","wikidata":"https://www.wikidata.org/wiki/Q7573786","display_name":"Sparse PCA","level":3,"score":0.5352557301521301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4937301576137543},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.48407426476478577},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4697941541671753},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.46967941522598267},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4516248106956482},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4399033784866333},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41027671098709106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3661883473396301},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36213192343711853},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0902671217918396},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3332183","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3332183","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5248355983","display_name":null,"funder_award_id":"61772048, 61672071, U1811463, 61632006, 61876012, and 61806014","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":39,"referenced_works":["https://openalex.org/W108615132","https://openalex.org/W1503398984","https://openalex.org/W1544957847","https://openalex.org/W1742512077","https://openalex.org/W1959608418","https://openalex.org/W1972490990","https://openalex.org/W1994443280","https://openalex.org/W2001141328","https://openalex.org/W2017588182","https://openalex.org/W2036887984","https://openalex.org/W2053186076","https://openalex.org/W2075228822","https://openalex.org/W2076137473","https://openalex.org/W2089468765","https://openalex.org/W2100495367","https://openalex.org/W2103914106","https://openalex.org/W2105732805","https://openalex.org/W2117553576","https://openalex.org/W2123421115","https://openalex.org/W2134312057","https://openalex.org/W2139916508","https://openalex.org/W2140095548","https://openalex.org/W2145072179","https://openalex.org/W2146966357","https://openalex.org/W2154225359","https://openalex.org/W2155008293","https://openalex.org/W2155178156","https://openalex.org/W2158170260","https://openalex.org/W2167623372","https://openalex.org/W2343166405","https://openalex.org/W2554071524","https://openalex.org/W2594379800","https://openalex.org/W2912990735","https://openalex.org/W2963917064","https://openalex.org/W2999320195","https://openalex.org/W3146199030","https://openalex.org/W4249502209","https://openalex.org/W6676014314","https://openalex.org/W6678628999"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W4391160746","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W1576424959","https://openalex.org/W3089231081"],"abstract_inverted_index":{"Dimensionality":[0],"reduction":[1,14,64],"is":[2,119,148],"widely":[3,152],"used":[4,153],"to":[5,123,150],"deal":[6],"with":[7],"high-dimensional":[8],"data.":[9],"As":[10],"a":[11,61,133],"famous":[12],"dimensionality":[13,63,91],"method,":[15,127],"principal":[16],"component":[17],"analysis":[18],"(PCA)":[19],"aiming":[20],"at":[21],"finding":[22],"the":[23,50,83,88,104,125,145],"low":[24],"dimension":[25],"feature":[26],"of":[27,53,90,103,107],"original":[28],"data":[29,54,110],"has":[30],"made":[31],"great":[32],"successes,":[33],"and":[34,136],"many":[35],"improved":[36],"PCA":[37,47,69],"algorithms":[38,44],"have":[39],"been":[40],"proposed.":[41],"However,":[42],"most":[43],"based":[45,70],"on":[46,71,131],"only":[48],"consider":[49],"linear":[51],"correlation":[52,79,98],"features.":[55,111],"In":[56,121],"this":[57],"article,":[58],"we":[59,128],"propose":[60],"novel":[62],"model":[65,95,117],"called":[66],"maximally":[67],"correlated":[68],"deep":[72,84],"parameterization":[73,85],"learning":[74],"(MCPCADP),":[75],"which":[76],"takes":[77],"nonlinear":[78,97],"into":[80],"account":[81],"in":[82],"framework":[86],"for":[87,116],"purpose":[89],"reduction.":[92],"The":[93,140],"new":[94,113],"explores":[96],"by":[99],"maximizing":[100],"Ky-Fan":[101],"norm":[102],"covariance":[105],"matrix":[106],"nonlinearly":[108],"mapped":[109],"A":[112],"BP":[114],"algorithm":[115,147],"optimization":[118],"derived.":[120],"order":[122],"assess":[124],"proposed":[126,146],"conduct":[129],"experiments":[130],"both":[132],"synthetic":[134],"database":[135],"several":[137,151],"real-world":[138],"databases.":[139],"experimental":[141],"results":[142],"demonstrate":[143],"that":[144],"comparable":[149],"algorithms.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}