{"id":"https://openalex.org/W4304091711","doi":"https://doi.org/10.1145/3503161.3547940","title":"Adaptively-weighted Integral Space for Fast Multiview Clustering","display_name":"Adaptively-weighted Integral Space for Fast Multiview Clustering","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304091711","doi":"https://doi.org/10.1145/3503161.3547940"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547940","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547940","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101982673","display_name":"Man-Sheng Chen","orcid":"https://orcid.org/0000-0001-6578-0616"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Man-Sheng Chen","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101433409","display_name":"Tuo Liu","orcid":"https://orcid.org/0000-0002-2821-1868"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tuo Liu","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076335744","display_name":"Chang\u2010Dong Wang","orcid":"https://orcid.org/0000-0001-5972-559X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang-Dong Wang","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050270789","display_name":"Dong Huang","orcid":"https://orcid.org/0000-0003-3923-8828"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Huang","raw_affiliation_strings":["South China Agricultural University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Agricultural University, Guangzhou, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034685928","display_name":"Jianhuang Lai","orcid":"https://orcid.org/0000-0003-3883-2024"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Huang Lai","raw_affiliation_strings":["Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3774","last_page":"3782"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9965999722480774,"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":0.9965999722480774,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.994700014591217,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9829000234603882,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7672519683837891},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5222724080085754},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.4709435701370239},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.45617079734802246},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45139241218566895},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4487362504005432},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4390827715396881},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4285348951816559},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.4246372878551483},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42222845554351807},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.4134214222431183},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35514169931411743},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.33988499641418457},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3380647897720337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.304107666015625},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.2075589895248413},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.10697239637374878}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7672519683837891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5222724080085754},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.4709435701370239},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.45617079734802246},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45139241218566895},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4487362504005432},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4390827715396881},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4285348951816559},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.4246372878551483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42222845554351807},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.4134214222431183},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35514169931411743},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.33988499641418457},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3380647897720337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.304107666015625},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.2075589895248413},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10697239637374878},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547940","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547940","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G611205877","display_name":null,"funder_award_id":"2021YFF1201200","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W297461772","https://openalex.org/W1923184257","https://openalex.org/W2035299679","https://openalex.org/W2043545458","https://openalex.org/W2113333558","https://openalex.org/W2128532956","https://openalex.org/W2128603141","https://openalex.org/W2559997609","https://openalex.org/W2604723872","https://openalex.org/W2605058302","https://openalex.org/W2740464254","https://openalex.org/W2766642606","https://openalex.org/W2787885212","https://openalex.org/W2913668833","https://openalex.org/W2921065608","https://openalex.org/W2965510190","https://openalex.org/W2988613494","https://openalex.org/W3002166600","https://openalex.org/W3024343454","https://openalex.org/W3091857251","https://openalex.org/W3117265891","https://openalex.org/W3155052204","https://openalex.org/W3206934290","https://openalex.org/W3207537336","https://openalex.org/W4200226646"],"related_works":["https://openalex.org/W3038204311","https://openalex.org/W2186101196","https://openalex.org/W3039354370","https://openalex.org/W2387908128","https://openalex.org/W2981254835","https://openalex.org/W3146523624","https://openalex.org/W2951567704","https://openalex.org/W4300978037","https://openalex.org/W4312142847","https://openalex.org/W2969974905"],"abstract_inverted_index":{"Multiview":[0],"clustering":[1,15,64],"has":[2],"been":[3,70],"extensively":[4],"studied":[5],"to":[6,12,73,118,142,175,202,214,222],"take":[7],"advantage":[8],"of":[9,20,101,148,240],"multi-source":[10],"information":[11],"improve":[13],"the":[14,21,36,44,93,97,105,110,120,124,131,177,181,204,216,227,238,241,247],"performance.":[16],"In":[17,146],"general,":[18],"most":[19],"existing":[22],"works":[23],"typically":[24],"compute":[25],"an":[26,152],"$n\\times":[27],"n$":[28],"affinity":[29],"graph":[30],"by":[31,80,88],"some":[32],"similarity/distance":[33],"metrics":[34],"(e.g.":[35],"Euclidean":[37],"distance)":[38],"or":[39,53,87],"learned":[40],"representations,":[41],"and":[42,196],"explore":[43],"pairwise":[45],"correlations":[46],"across":[47],"views.":[48],"But":[49],"unfortunately,":[50],"a":[51,190],"quadratic":[52],"even":[54],"cubic":[55],"complexity":[56],"is":[57,116,139,212,220],"often":[58],"needed,":[59],"bringing":[60],"about":[61],"difficulty":[62],"in":[63,77],"large-scale":[65],"datasets.":[66],"Some":[67],"efforts":[68],"have":[69,103,223],"made":[71],"recently":[72],"capture":[74],"data":[75],"distribution":[76],"multiple":[78,136],"views":[79,138],"selecting":[81],"view-wise":[82],"anchor":[83],"representations":[84],"with":[85,165,185,193,246],"k-means,":[86],"direct":[89],"matrix":[90],"factorization":[91],"on":[92,233],"original":[94],"observations.":[95],"Despite":[96],"significant":[98],"success,":[99],"few":[100],"them":[102],"considered":[104],"view-insufficiency":[106],"issue,":[107],"implicitly":[108],"holding":[109],"assumption":[111],"that":[112],"each":[113],"individual":[114],"view":[115,147,170,178],"sufficient":[117],"recover":[119],"cluster":[121,133,197],"structure.":[122],"Moreover,":[123],"latent":[125,182,205],"integral":[126,183,206],"space":[127,184],"as":[128,130],"well":[129],"shared":[132],"structure":[134],"from":[135,180],"insufficient":[137],"not":[140],"able":[141],"be":[143],"simultaneously":[144],"discovered.":[145],"this,":[149],"we":[150],"propose":[151],"\\underlineA":[153],"daptively-weighted":[154],"\\underlineI":[155],"ntegral":[156],"Space":[157],"for":[158],"Fast":[159],"\\underlineM":[160],"ultiview":[161],"\\underlineC":[162],"lustering":[163],"(AIMC)":[164],"nearly":[166],"linear":[167,224],"complexity.":[168],"Specifically,":[169],"generation":[171],"models":[172],"are":[173,199],"designed":[174],"reconstruct":[176],"observations":[179],"diverse":[186],"adaptive":[187],"contributions.":[188],"Meanwhile,":[189],"centroid":[191],"representation":[192],"orthogonality":[194],"constraint":[195],"partition":[198],"seamlessly":[200],"constructed":[201],"approximate":[203],"space.":[207],"An":[208],"alternate":[209],"minimizing":[210],"algorithm":[211],"developed":[213],"solve":[215],"optimization":[217],"problem,":[218],"which":[219],"proved":[221],"time":[225],"complexityw.r.t.":[226],"sample":[228],"size.":[229],"Extensive":[230],"experiments":[231],"conducted":[232],"several":[234],"real-world":[235],"datasets":[236],"confirm":[237],"superiority":[239],"proposed":[242],"AIMC":[243],"method":[244],"compared":[245],"state-of-the-art":[248],"methods.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}