{"id":"https://openalex.org/W7138096252","doi":"https://doi.org/10.1609/aaai.v40i31.39865","title":"Cross-view Anchor Graph Learning and Factorization for Incomplete Multi-view Clustering","display_name":"Cross-view Anchor Graph Learning and Factorization for Incomplete Multi-view Clustering","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138096252","doi":"https://doi.org/10.1609/aaai.v40i31.39865"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i31.39865","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i31.39865","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i31.39865","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129657672","display_name":"Xinxin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinxin Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038549098","display_name":"Yongshan Zhang","orcid":"https://orcid.org/0000-0001-5817-1732"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongshan Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129730508","display_name":"Xiaochen Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaochen Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129671110","display_name":"Yicong Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yicong Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.2739,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96762819,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"40","issue":"31","first_page":"26570","last_page":"26578"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.5497999787330627,"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":0.5497999787330627,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.12070000171661377,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.07509999722242355,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6862000226974487},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5595999956130981},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5561000108718872},{"id":"https://openalex.org/keywords/graph-factorization","display_name":"Graph factorization","score":0.3578999936580658},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.35569998621940613},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.34119999408721924},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.326200008392334},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.30059999227523804}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6862000226974487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6194000244140625},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5561000108718872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4325000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4311999976634979},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42730000615119934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41130000352859497},{"id":"https://openalex.org/C128115575","wikidata":"https://www.wikidata.org/wiki/Q5597083","display_name":"Graph factorization","level":5,"score":0.3578999936580658},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.35569998621940613},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.34119999408721924},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2969000041484833},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.29170000553131104},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.29030001163482666},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.25099998712539673},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i31.39865","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i31.39865","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/39865","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/39865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i31.39865","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i31.39865","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320325571","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph-based":[0],"incomplete":[1],"multi-view":[2],"clustering":[3,13],"algorithms":[4],"have":[5],"gathered":[6],"much":[7],"attention":[8],"due":[9],"to":[10,41,53,109,151],"their":[11],"impressive":[12],"performance.":[14],"However,":[15],"existing":[16],"methods":[17,38],"primarily":[18],"leverage":[19],"intra-view":[20],"correlation":[21],"from":[22,128],"observed":[23],"views,":[24],"while":[25],"ignoring":[26],"the":[27,45,82,105,119,132,136,153,162],"exploration":[28],"of":[29,87,100,121,138,164],"explicit":[30],"compensation":[31,126],"relationships":[32],"between":[33],"different":[34],"views.":[35],"Moreover,":[36],"these":[37,58],"need":[39],"post-processing":[40],"get":[42],"labels,":[43,102],"and":[44,67,114],"separate":[46],"steps":[47],"lack":[48],"negotiation,":[49],"which":[50],"may":[51],"lead":[52],"sub-optimal":[54],"solutions.":[55],"To":[56,116],"address":[57],"issues,":[59],"we":[60],"propose":[61],"a":[62,97,139],"Cross-view":[63],"Anchor":[64,74],"Graph":[65,75],"Learning":[66],"Factorization":[68],"(AGLF)":[69],"method.":[70,166],"AGLF":[71,124],"develops":[72],"an":[73],"Completion":[76],"(AGC)":[77],"framework":[78],"that":[79],"explicitly":[80],"learn":[81],"missing":[83],"subgraph":[84,122],"structures.":[85],"Instead":[86],"requiring":[88],"post-processing,":[89],"AGC":[90,133],"directly":[91],"produces":[92],"soft":[93,101],"labels.":[94],"By":[95],"establishing":[96],"third-order":[98],"tensor":[99,106],"it":[103],"employs":[104],"Schatten":[107],"p-norm":[108],"enhance":[110],"anchor":[111,141],"graph":[112,142],"learning":[113],"factorization.":[115],"significantly":[117],"improve":[118],"quality":[120],"learning,":[123],"incorporates":[125],"subgraphs":[127],"supplementary":[129],"views":[130],"into":[131],"framework,":[134],"enabling":[135],"construction":[137],"better":[140],"for":[143],"label":[144],"learning.":[145],"An":[146],"optimization":[147],"algorithm":[148],"is":[149],"devised":[150],"solve":[152],"objective":[154],"function.":[155],"Experimental":[156],"results":[157],"across":[158],"various":[159],"datasets":[160],"demonstrate":[161],"effectiveness":[163],"our":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-03-18T00:00:00"}
