{"id":"https://openalex.org/W4415178827","doi":"https://doi.org/10.1109/tnnls.2025.3617888","title":"PGFormer: A Prototype-Graph Transformer for Incomplete Multiview Clustering","display_name":"PGFormer: A Prototype-Graph Transformer for Incomplete Multiview Clustering","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W4415178827","doi":"https://doi.org/10.1109/tnnls.2025.3617888","pmid":"https://pubmed.ncbi.nlm.nih.gov/41086073"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3617888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3617888","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5010787595","display_name":"Yiming Du","orcid":"https://orcid.org/0000-0001-6613-2207"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiming Du","raw_affiliation_strings":["Department of Computer Science, Old Dominion University, Norfolk, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781579","display_name":"Yao Wang","orcid":"https://orcid.org/0000-0002-4841-4232"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["Department of Computer Science, Old Dominion University, Norfolk, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ziyu Wang","orcid":"https://orcid.org/0009-0006-0199-1231"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyu Wang","raw_affiliation_strings":["Department of Computer Science, Old Dominion University, Norfolk, VA, USA"],"raw_orcid":"https://orcid.org/0009-0006-0199-1231","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061056153","display_name":"Rui Ning","orcid":"https://orcid.org/0000-0003-4050-6252"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Ning","raw_affiliation_strings":["Department of Computer Science, Old Dominion University, Norfolk, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006167598","display_name":"Lusi Li","orcid":"https://orcid.org/0000-0002-4323-2632"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lusi Li","raw_affiliation_strings":["Department of Computer Science, Old Dominion University, Norfolk, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-4323-2632","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I81365321"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14433837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"37","issue":"3","first_page":"1163","last_page":"1175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9835000038146973,"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"}},"topics":[{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9835000038146973,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9815000295639038,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9728000164031982,"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":0.722100019454956},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6751000285148621},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.6355000138282776},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.6061000227928162},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48840001225471497},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4056999981403351},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.3905999958515167},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.38659998774528503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7299000024795532},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.722100019454956},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6751000285148621},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6355000138282776},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.6061000227928162},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5555999875068665},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4661000072956085},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4056999981403351},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3905999958515167},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3440000116825104},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29350000619888306},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3617888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3617888","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:41086073","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41086073","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7517489523","display_name":null,"funder_award_id":"CNS-2153358","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8562260746","display_name":null,"funder_award_id":"CNS-2245918","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307868","display_name":"Commonwealth Foundation","ror":"https://ror.org/05rhej692"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Incomplete":[0],"multiview":[1],"clustering":[2,57,82],"(IMVC)":[3],"faces":[4],"significant":[5],"challenges":[6],"due":[7],"to":[8,37,41,80,88,101,123,182,200],"missing":[9,53,167,184],"data":[10],"and":[11,32,48,105,127,138,150],"inherent":[12],"view":[13,30],"discrepancies.":[14],"While":[15],"deep":[16],"neural":[17],"networks":[18],"offer":[19],"powerful":[20],"representation":[21],"learning":[22],"capabilities":[23],"for":[24,52],"IMVC,":[25],"existing":[26],"methods":[27],"often":[28],"overlook":[29],"diversity":[31],"force":[33],"representations":[34,44],"across":[35,204],"views":[36,181],"be":[38],"identical,":[39],"leading":[40],"1)":[42],"biased":[43],"with":[45,216],"distorted":[46],"topologies":[47,126],"2)":[49],"inaccurate":[50],"imputation":[51,171],"data,":[54,168],"ultimately":[55],"degrading":[56],"performance.":[58,83],"To":[59,165],"address":[60,166],"these":[61,110],"issues,":[62],"we":[63],"propose":[64],"prototype-graph":[65],"transformer":[66],"(PGFormer),":[67],"a":[68,98,118,155],"novel":[69],"IMVC":[70],"framework":[71],"that":[72,209],"integrates":[73],"prototype":[74,177,198],"assignments,":[75],"rather":[76],"than":[77],"direct":[78],"representations,":[79],"enhance":[81],"PGFormer":[84,99,116,210],"leverages":[85,174],"view-specific":[86,136],"encoders":[87],"extract":[89],"features":[90],"from":[91,131,179],"available":[92,107],"samples":[93,108,185],"in":[94],"each":[95,114,163],"view,":[96,115],"employs":[97],"designed":[100],"refine":[102],"node":[103,133],"embeddings,":[104],"reconstructs":[106],"using":[109,186],"refined":[111,142,187],"embeddings.":[112,134],"For":[113],"utilizes":[117],"graph":[119],"convolutional":[120],"network":[121],"(GCN)":[122],"model":[124],"node-to-node":[125],"generate":[128],"semantic":[129],"prototypes":[130,137],"the":[132,169,175,193,217],"These":[135],"embeddings":[139],"are":[140],"then":[141],"through":[143],"dual":[144],"attention":[145],"mechanisms:":[146],"prototype-to-prototype":[147],"(P2P)":[148],"self-attention":[149],"prototype-to-node":[151],"(P2N)":[152],"cross-attention,":[153],"enabling":[154],"thorough":[156],"exploration":[157],"of":[158],"multilevel":[159],"topological":[160],"relationships":[161],"within":[162],"view.":[164],"cross-prototype":[170],"(CPI)":[172],"module":[173,196],"weighted":[176],"assignments":[178,199],"different":[180],"impute":[183],"intraview":[188],"prototypes.":[189],"Building":[190],"on":[191],"this,":[192],"cross-view":[194],"alignment":[195],"calibrates":[197],"ensure":[201],"consistent":[202],"predictions":[203],"views.":[205],"Extensive":[206],"experiments":[207],"demonstrate":[208],"can":[211],"achieve":[212],"superior":[213],"performance":[214],"compared":[215],"baselines.":[218]},"counts_by_year":[],"updated_date":"2026-03-03T06:13:14.889584","created_date":"2025-10-15T00:00:00"}