{"id":"https://openalex.org/W4312507377","doi":"https://doi.org/10.1109/tkde.2022.3230964","title":"Low-Rank Tensor Learning for Incomplete Multiview Clustering","display_name":"Low-Rank Tensor Learning for Incomplete Multiview Clustering","publication_year":2022,"publication_date":"2022-12-21","ids":{"openalex":"https://openalex.org/W4312507377","doi":"https://doi.org/10.1109/tkde.2022.3230964"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2022.3230964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3230964","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","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/A5100332992","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0003-0827-8819"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Chen","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100439565","display_name":"Zhu Wang","orcid":"https://orcid.org/0000-0002-7752-3606"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhu Wang","raw_affiliation_strings":["Law School, Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Law School, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052650235","display_name":"Hua Mao","orcid":"https://orcid.org/0000-0003-3198-6282"},"institutions":[{"id":"https://openalex.org/I32394136","display_name":"Northumbria University","ror":"https://ror.org/049e6bc10","country_code":"GB","type":"education","lineage":["https://openalex.org/I32394136"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hua Mao","raw_affiliation_strings":["Department of Computer and Information Sciences, Northumbria University, Newcastle, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Northumbria University, Newcastle, U.K","institution_ids":["https://openalex.org/I32394136"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022800038","display_name":"Xi Peng","orcid":"https://orcid.org/0000-0002-5727-2790"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Peng","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100332992"],"corresponding_institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210125143"],"apc_list":null,"apc_paid":null,"fwci":1.8375,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.86931818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"35","issue":"11","first_page":"11556","last_page":"11569"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.970300018787384,"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":0.970300018787384,"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/T12303","display_name":"Tensor decomposition and applications","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9610999822616577,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6898684501647949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6439352035522461},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6285766363143921},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.587049126625061},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5588826537132263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5148881673812866},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.49436426162719727},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.48893943428993225},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.45915356278419495},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.45000720024108887},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.44766613841056824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4230196475982666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35420727729797363},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2674575448036194},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.1615281105041504}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6898684501647949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6439352035522461},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6285766363143921},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.587049126625061},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5588826537132263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5148881673812866},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.49436426162719727},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.48893943428993225},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.45915356278419495},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.45000720024108887},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.44766613841056824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4230196475982666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35420727729797363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2674575448036194},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.1615281105041504},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2022.3230964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3230964","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2570225771","display_name":null,"funder_award_id":"62176171","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4605825167","display_name":null,"funder_award_id":"U21B2040","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6996121907","display_name":null,"funder_award_id":"61303015","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":59,"referenced_works":["https://openalex.org/W62192748","https://openalex.org/W188881548","https://openalex.org/W191001584","https://openalex.org/W1566135517","https://openalex.org/W1655434153","https://openalex.org/W1923184257","https://openalex.org/W1992426838","https://openalex.org/W2037549374","https://openalex.org/W2043571470","https://openalex.org/W2098693229","https://openalex.org/W2121947440","https://openalex.org/W2150856297","https://openalex.org/W2161160262","https://openalex.org/W2329838488","https://openalex.org/W2565835729","https://openalex.org/W2787425086","https://openalex.org/W2788448133","https://openalex.org/W2807957280","https://openalex.org/W2809299793","https://openalex.org/W2894142921","https://openalex.org/W2895210058","https://openalex.org/W2899604627","https://openalex.org/W2963885538","https://openalex.org/W2964199994","https://openalex.org/W2964837208","https://openalex.org/W2965322655","https://openalex.org/W2970240670","https://openalex.org/W3005916310","https://openalex.org/W3007380221","https://openalex.org/W3008561560","https://openalex.org/W3014514478","https://openalex.org/W3015232403","https://openalex.org/W3015899030","https://openalex.org/W3021342796","https://openalex.org/W3045903937","https://openalex.org/W3098802639","https://openalex.org/W3100676321","https://openalex.org/W3120740533","https://openalex.org/W3137030179","https://openalex.org/W3156905473","https://openalex.org/W3170699884","https://openalex.org/W3176728172","https://openalex.org/W3196973577","https://openalex.org/W3206460183","https://openalex.org/W3208283713","https://openalex.org/W4210518188","https://openalex.org/W4214544745","https://openalex.org/W4214876032","https://openalex.org/W4220873374","https://openalex.org/W4225942313","https://openalex.org/W4284968770","https://openalex.org/W4289674194","https://openalex.org/W4292363360","https://openalex.org/W4367280449","https://openalex.org/W6774220812","https://openalex.org/W6784910987","https://openalex.org/W6785382328","https://openalex.org/W6788247690","https://openalex.org/W6841022506"],"related_works":["https://openalex.org/W2941091020","https://openalex.org/W2962759270","https://openalex.org/W3104816361","https://openalex.org/W2772423858","https://openalex.org/W4391266752","https://openalex.org/W2898722594","https://openalex.org/W4226139248","https://openalex.org/W2125326641","https://openalex.org/W3016208341","https://openalex.org/W2201180357"],"abstract_inverted_index":{"Incomplete":[0],"multiview":[1,15,131,137],"clustering":[2],"(IMVC)":[3],"is":[4,183],"an":[5,179],"effective":[6],"way":[7],"to":[8,91,152],"identify":[9],"the":[10,34,38,52,85,112,124,167,175,191],"underlying":[11],"structure":[12],"of":[13,33,43,84,88,129,178],"incomplete":[14,130],"data.":[16,132],"Most":[17],"existing":[18],"algorithms":[19],"based":[20],"on":[21],"matrix":[22,77,139],"factorization,":[23],"graph":[24],"learning":[25,28,68],"or":[26],"subspace":[27],"have":[29],"at":[30],"least":[31],"one":[32],"following":[35],"limitations:":[36],"(1)":[37],"global":[39,125],"and":[40,101,126],"local":[41,127],"structures":[42,128],"high-dimensional":[44,89],"data":[45,90],"are":[46,58],"not":[47],"effectively":[48],"explored":[49],"simultaneously;":[50],"(2)":[51],"high-order":[53,168],"correlations":[54,169],"among":[55,170],"multiple":[56,171],"views":[57,98],"ignored.":[59],"In":[60,173],"this":[61],"article,":[62],"we":[63,134],"propose":[64],"a":[65,73,136,149,154],"low-rank":[66,100],"tensor":[67,151],"(LRTL)":[69],"method":[70,194],"that":[71,142,190],"learns":[72],"consensus":[74,155],"low-dimensional":[75,105,145,156],"embedding":[76,106,118,138,146,157],"for":[78,96],"IMVC.":[79],"We":[80],"first":[81],"take":[82],"advantage":[83],"self-expressiveness":[86],"property":[87],"construct":[92],"sparse":[93,113],"similarity":[94,114],"matrices":[95,107,115,147],"individual":[97,144],"under":[99],"sparsity":[102],"constraints.":[103],"Individual":[104],"can":[108],"be":[109],"obtained":[110],"from":[111],"using":[116],"spectral":[117],"techniques.":[119],"This":[120],"approach":[121],"simultaneously":[122],"explores":[123],"Then,":[133],"present":[135],"fusion":[140,160,181],"model":[141,161],"incorporates":[143],"into":[148],"third-norm":[150],"achieve":[153],"matrix.":[158],"The":[159],"exploits":[162],"complementary":[163],"information":[164],"by":[165],"finding":[166],"views.":[172],"addition,":[174],"computational":[176],"cost":[177],"improved":[180],"strategy":[182],"dramatically":[184],"reduced.":[185],"Extensive":[186],"experimental":[187],"results":[188],"demonstrate":[189],"proposed":[192],"LRTL":[193],"outperforms":[195],"several":[196],"state-of-the-art":[197],"approaches.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}