{"id":"https://openalex.org/W3157704640","doi":"https://doi.org/10.1007/s10994-022-06194-9","title":"Representation learning for clustering via building consensus","display_name":"Representation learning for clustering via building consensus","publication_year":2022,"publication_date":"2022-09-09","ids":{"openalex":"https://openalex.org/W3157704640","doi":"https://doi.org/10.1007/s10994-022-06194-9","mag":"3157704640"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-022-06194-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06194-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06194-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06194-9.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035432690","display_name":"Aniket Anand Deshmukh","orcid":"https://orcid.org/0000-0002-7292-8436"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aniket Anand Deshmukh","raw_affiliation_strings":["Microsoft, Mountain View, CA, 94043, USA"],"raw_orcid":"https://orcid.org/0000-0002-7292-8436","affiliations":[{"raw_affiliation_string":"Microsoft, Mountain View, CA, 94043, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046845999","display_name":"Jayanth Reddy Regatti","orcid":"https://orcid.org/0000-0001-8150-2288"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jayanth Reddy Regatti","raw_affiliation_strings":["The Ohio State University, Columbus, OH, 43210, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Ohio State University, Columbus, OH, 43210, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079433953","display_name":"Eren Manavoglu","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eren Manavoglu","raw_affiliation_strings":["Microsoft, Mountain View, CA, 94043, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Mountain View, CA, 94043, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086993787","display_name":"\u00dcr\u00fcn Do\u01e7an","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Urun Dogan","raw_affiliation_strings":["Microsoft, Mountain View, CA, 94043, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Mountain View, CA, 94043, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035432690"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.8323,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.76571904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"111","issue":"12","first_page":"4601","last_page":"4638"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9975000023841858,"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/T10057","display_name":"Face and Expression Recognition","score":0.9918000102043152,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.9004400968551636},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6450839042663574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6276256442070007},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6168312430381775},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6010873913764954},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.5843381285667419},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5486125349998474},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5278140306472778},{"id":"https://openalex.org/keywords/conceptual-clustering","display_name":"Conceptual clustering","score":0.5092815160751343},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4828660786151886},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.48177286982536316},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.47505617141723633},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4723823070526123},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46715086698532104},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.45245277881622314},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.43914735317230225},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.403558611869812}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.9004400968551636},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6450839042663574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6276256442070007},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6168312430381775},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6010873913764954},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.5843381285667419},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5486125349998474},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5278140306472778},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.5092815160751343},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4828660786151886},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.48177286982536316},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.47505617141723633},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4723823070526123},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46715086698532104},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.45245277881622314},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.43914735317230225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.403558611869812},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-022-06194-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06194-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06194-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10994-022-06194-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06194-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06194-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3157704640.pdf","grobid_xml":"https://content.openalex.org/works/W3157704640.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W178113997","https://openalex.org/W1971784203","https://openalex.org/W1992419399","https://openalex.org/W2041670460","https://openalex.org/W2108598243","https://openalex.org/W2110798204","https://openalex.org/W2127218421","https://openalex.org/W2132377940","https://openalex.org/W2137206388","https://openalex.org/W2139280638","https://openalex.org/W2145094598","https://openalex.org/W2153233077","https://openalex.org/W2158131535","https://openalex.org/W2165232124","https://openalex.org/W2165874743","https://openalex.org/W2169446650","https://openalex.org/W2222512263","https://openalex.org/W2293078015","https://openalex.org/W2335920499","https://openalex.org/W2741885505","https://openalex.org/W2779692282","https://openalex.org/W2786958346","https://openalex.org/W2788552663","https://openalex.org/W2798991696","https://openalex.org/W2883725317","https://openalex.org/W2919115771","https://openalex.org/W2942985259","https://openalex.org/W2954996726","https://openalex.org/W2962852342","https://openalex.org/W2964074409","https://openalex.org/W2990500698","https://openalex.org/W2994560339","https://openalex.org/W2995181141","https://openalex.org/W2998388430","https://openalex.org/W3005680577","https://openalex.org/W3034363127","https://openalex.org/W3035060554","https://openalex.org/W3035524453","https://openalex.org/W3036224891","https://openalex.org/W3039616371","https://openalex.org/W3087124270","https://openalex.org/W3106428938","https://openalex.org/W3106709020","https://openalex.org/W3110446398","https://openalex.org/W3137513727","https://openalex.org/W3199244260","https://openalex.org/W4236765470","https://openalex.org/W4285719527","https://openalex.org/W6684578312","https://openalex.org/W6685380521","https://openalex.org/W6703565433","https://openalex.org/W6762257159","https://openalex.org/W6770641979"],"related_works":["https://openalex.org/W2160785859","https://openalex.org/W3140018618","https://openalex.org/W2622412490","https://openalex.org/W3186815950","https://openalex.org/W3161536855","https://openalex.org/W2607137685","https://openalex.org/W1970969031","https://openalex.org/W2049890817","https://openalex.org/W4225777930","https://openalex.org/W2107260502"],"abstract_inverted_index":{"Abstract":[0],"In":[1],"this":[2],"paper,":[3],"we":[4,149],"focus":[5],"on":[6,25,141],"unsupervised":[7,20,129],"representation":[8,21,45,83,106,130],"learning":[9,22,121,131],"for":[10,79,154,182],"clustering":[11,18,86,94,99,127,136,155,163,168],"of":[12,31,64,91,138,144,186],"images.":[13],"Recent":[14],"advances":[15],"in":[16,43,81,104,161,170],"deep":[17],"and":[19,108,116,192],"are":[23,73,196],"based":[24],"the":[26,44,82,105,135,151,158,187,193],"idea":[27],"that":[28,71],"different":[29,85,89],"views":[30],"an":[32,61,119],"input":[33],"image":[34,146],"(generated":[35],"through":[36],"data":[37],"augmentation":[38],"techniques)":[39],"must":[40,52],"be":[41],"close":[42],"space":[46,107],"(exemplar":[47],"consistency),":[48],"and/or":[49],"similar":[50,54,77],"images":[51],"have":[53],"cluster":[55],"assignments":[56],"(population":[57],"consistency).":[58],"We":[59,96,175],"define":[60,97],"additional":[62],"notion":[63],"consistency,":[65],"consensus":[66,126],"consistency":[67],",":[68],"which":[69],"ensures":[70],"representations":[72],"learned":[74],"to":[75,156],"induce":[76],"partitions":[78],"variations":[80,103],"space,":[84],"algorithms":[87],"or":[88],"initializations":[90],"a":[92,98,178,183],"single":[93],"algorithm.":[95,189],"loss":[100],"by":[101],"executing":[102],"seamlessly":[109],"integrate":[110],"all":[111],"three":[112],"consistencies":[113],"(consensus,":[114],"exemplar":[115],"population)":[117],"into":[118],"end-to-end":[120],"framework.":[122],"The":[123,190],"proposed":[124,188],"algorithm,":[125],"using":[128],"(ConCURL),":[132],"improves":[133],"upon":[134],"performance":[137,169],"state-of-the-art":[139],"methods":[140],"four":[142],"out":[143],"five":[145],"datasets.":[147],"Furthermore,":[148],"extend":[150],"evaluation":[152],"procedure":[153],"reflect":[157],"challenges":[159],"encountered":[160],"real-world":[162],"tasks,":[164],"such":[165],"as":[166],"maintaining":[167],"cases":[171],"with":[172],"distribution":[173],"shifts.":[174],"also":[176],"perform":[177],"detailed":[179],"ablation":[180],"study":[181],"deeper":[184],"understanding":[185],"code":[191],"trained":[194],"models":[195],"available":[197],"at":[198],"https://github.com/JayanthRR/ConCURL_NCE":[199],".":[200]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}