{"id":"https://openalex.org/W3202648776","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534368","title":"Learning Associations between Features and Clusters: An Interpretable Deep Clustering Method","display_name":"Learning Associations between Features and Clusters: An Interpretable Deep Clustering Method","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3202648776","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534368","mag":"3202648776"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534368","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534368","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.osti.gov/biblio/1827282","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082976295","display_name":"Hao Huang","orcid":"https://orcid.org/0000-0002-3777-1488"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hao Huang","raw_affiliation_strings":["GE Global Research,San Ramon,CA,USA"],"affiliations":[{"raw_affiliation_string":"GE Global Research,San Ramon,CA,USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008564522","display_name":"Feng Xue","orcid":"https://orcid.org/0000-0003-2828-1476"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Xue","raw_affiliation_strings":["GE Global Research,Niskayuna,NY,USA"],"affiliations":[{"raw_affiliation_string":"GE Global Research,Niskayuna,NY,USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073342710","display_name":"Weizhong Yan","orcid":"https://orcid.org/0000-0002-7916-8476"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weizhong Yan","raw_affiliation_strings":["GE Global Research,Niskayuna,NY,USA"],"affiliations":[{"raw_affiliation_string":"GE Global Research,Niskayuna,NY,USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112225428","display_name":"Tianyi Wang","orcid":"https://orcid.org/0009-0007-1844-8582"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Wang","raw_affiliation_strings":["GE Global Research,Niskayuna,NY,USA"],"affiliations":[{"raw_affiliation_string":"GE Global Research,Niskayuna,NY,USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048176207","display_name":"Shinjae Yoo","orcid":"https://orcid.org/0000-0003-4378-6448"},"institutions":[{"id":"https://openalex.org/I200870766","display_name":"Brookhaven National Laboratory","ror":"https://ror.org/02ex6cf31","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I200870766","https://openalex.org/I39565521","https://openalex.org/I4210142672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shinjae Yoo","raw_affiliation_strings":["Brookhaven National Laboratory, Upton, NY, USA"],"affiliations":[{"raw_affiliation_string":"Brookhaven National Laboratory, Upton, NY, USA","institution_ids":["https://openalex.org/I200870766"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112351454","display_name":"Chenxiao Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenxiao Xu","raw_affiliation_strings":["Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5082976295"],"corresponding_institution_ids":["https://openalex.org/I4210134512"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.64515879,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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":0.9987999796867371,"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":0.8413325548171997},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6092665195465088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5889731645584106},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5321627855300903},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5235861539840698},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4960831105709076},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.49575290083885193},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4804244041442871},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.42557772994041443},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3868681788444519},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24119430780410767}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8413325548171997},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6092665195465088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5889731645584106},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5321627855300903},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5235861539840698},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4960831105709076},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.49575290083885193},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4804244041442871},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.42557772994041443},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3868681788444519},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24119430780410767},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534368","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534368","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:osti.gov:1827282","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1827282","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},{"id":"pmh:oai:osti.gov:1842797","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1842797","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:osti.gov:1827282","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1827282","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":73,"referenced_works":["https://openalex.org/W117096852","https://openalex.org/W1489608363","https://openalex.org/W1565201084","https://openalex.org/W1571353513","https://openalex.org/W1969486090","https://openalex.org/W2003217181","https://openalex.org/W2006533296","https://openalex.org/W2008073424","https://openalex.org/W2046267801","https://openalex.org/W2048134425","https://openalex.org/W2097820631","https://openalex.org/W2112104211","https://openalex.org/W2119253710","https://openalex.org/W2120303002","https://openalex.org/W2129098897","https://openalex.org/W2132914434","https://openalex.org/W2159660986","https://openalex.org/W2161985854","https://openalex.org/W2194775991","https://openalex.org/W2294644361","https://openalex.org/W2533545350","https://openalex.org/W2551604710","https://openalex.org/W2556467266","https://openalex.org/W2571899125","https://openalex.org/W2575552627","https://openalex.org/W2741943936","https://openalex.org/W2781711557","https://openalex.org/W2809034148","https://openalex.org/W2883725317","https://openalex.org/W2884851420","https://openalex.org/W2915678294","https://openalex.org/W2922380854","https://openalex.org/W2922527181","https://openalex.org/W2939176882","https://openalex.org/W2944059327","https://openalex.org/W2952228992","https://openalex.org/W2953791858","https://openalex.org/W2962875092","https://openalex.org/W2962911132","https://openalex.org/W2963165023","https://openalex.org/W2963365397","https://openalex.org/W2963390294","https://openalex.org/W2963487393","https://openalex.org/W2963761396","https://openalex.org/W2963840432","https://openalex.org/W2964036159","https://openalex.org/W2964074409","https://openalex.org/W2964275228","https://openalex.org/W2970297355","https://openalex.org/W2970458417","https://openalex.org/W2970954308","https://openalex.org/W2971142628","https://openalex.org/W2987798608","https://openalex.org/W3004946360","https://openalex.org/W3097993951","https://openalex.org/W3101709902","https://openalex.org/W3120740533","https://openalex.org/W4213367101","https://openalex.org/W4250657332","https://openalex.org/W4289236186","https://openalex.org/W6604803494","https://openalex.org/W6629092883","https://openalex.org/W6652231383","https://openalex.org/W6674539747","https://openalex.org/W6685380521","https://openalex.org/W6728550200","https://openalex.org/W6729832310","https://openalex.org/W6732395776","https://openalex.org/W6744043827","https://openalex.org/W6755477582","https://openalex.org/W6760511867","https://openalex.org/W6767624375","https://openalex.org/W6767630037"],"related_works":["https://openalex.org/W4385270139","https://openalex.org/W3080491161","https://openalex.org/W2111119584","https://openalex.org/W3186815950","https://openalex.org/W2590034888","https://openalex.org/W4292621762","https://openalex.org/W3098102082","https://openalex.org/W2110877857","https://openalex.org/W2589483699","https://openalex.org/W3179062140"],"abstract_inverted_index":{"Clustering":[0],"is":[1,87],"a":[2,25],"challenging":[3],"problem":[4,69],"when":[5],"many":[6],"features":[7,42,133,149],"are":[8,46],"irrelevant":[9],"to":[10,18,89,106,122,156],"separate":[11],"clusters.":[12,128],"Also,":[13],"different":[14,116,127],"clusters":[15,35,61,135],"may":[16],"relate":[17],"various":[19],"feature":[20,93,102,117],"subsets.":[21],"This":[22],"work":[23],"proposes":[24],"deep":[26],"clustering":[27,141,159],"algorithm":[28],"that":[29,55],"localizes":[30],"the":[31,58,68,91,110,124,130,147,158],"search":[32],"for":[33],"instance":[34],"and":[36,134,139],"their":[37],"relevant":[38,41],"features.":[39],"The":[40,98],"of":[43,60,95,150],"each":[44,96,151],"cluster":[45,152],"defined":[47],"as":[48,70],"those":[49],"with":[50],"high":[51],"associations":[52,94,131],"(dependency)":[53],"within":[54],"cluster.":[56,97,112],"Given":[57],"number":[59],"<tex":[62,71],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[63,72],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$K$</tex>":[64,73],",":[65],"we":[66],"formulate":[67],".":[74],"-parallel":[75],"auto-reconstructive":[76],"learning,":[77,81],"where":[78],"low-rank":[79],"graph":[80,84],"rooted":[82],"in":[83],"Laplacian":[85],"theory,":[86],"used":[88,155],"explore":[90],"unknown":[92],"model":[99],"performs":[100],"automatic":[101],"weighting":[103],"on":[104],"residuals":[105],"minimize":[107],"loss":[108,125],"from":[109,126],"corresponding":[111],"Through":[113],"such":[114],"design,":[115],"subsets":[118],"can":[119,136,143,153],"be":[120,137,144,154],"learned":[121],"calculate":[123],"Subsequently,":[129],"between":[132],"acquired,":[138],"better":[140],"result":[142],"achieved.":[145],"Moreover,":[146],"associated":[148],"interpret":[157],"patterns.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
