{"id":"https://openalex.org/W2983356984","doi":"https://doi.org/10.1109/icip46576.2022.9897775","title":"Clustering by Directly Disentangling Latent Space","display_name":"Clustering by Directly Disentangling Latent Space","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W2983356984","doi":"https://doi.org/10.1109/icip46576.2022.9897775","mag":"2983356984"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897775","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897775","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5045995739","display_name":"Fei Ding","orcid":"https://orcid.org/0000-0002-8935-8169"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Ding","raw_affiliation_strings":["Clemson University,School of Computing,Clemson,SC,USA,29634"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,School of Computing,Clemson,SC,USA,29634","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034022345","display_name":"Yin Yang","orcid":"https://orcid.org/0000-0002-0549-3882"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yin Yang","raw_affiliation_strings":["Clemson University,School of Computing,Clemson,SC,USA,29634"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,School of Computing,Clemson,SC,USA,29634","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100683466","display_name":"Feng Luo","orcid":"https://orcid.org/0000-0002-4813-2403"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Luo","raw_affiliation_strings":["Clemson University,School of Computing,Clemson,SC,USA,29634"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,School of Computing,Clemson,SC,USA,29634","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":0.4623,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.70292637,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"70","issue":null,"first_page":"341","last_page":"345"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9969000220298767,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9969000220298767,"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":0.9958999752998352,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.991599977016449,"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":0.8949187994003296},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6754387617111206},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.613812267780304},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5403165817260742},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5299875140190125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5083009600639343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.50115966796875},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.44010215997695923},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4384559690952301},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4249211251735687},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.42027729749679565},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3966888189315796},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.2897856533527374},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.1166970431804657}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8949187994003296},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6754387617111206},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.613812267780304},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5403165817260742},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5299875140190125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5083009600639343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.50115966796875},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.44010215997695923},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4384559690952301},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4249211251735687},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.42027729749679565},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3966888189315796},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2897856533527374},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.1166970431804657},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897775","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897775","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W1902027874","https://openalex.org/W1959608418","https://openalex.org/W2019464758","https://openalex.org/W2097922870","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2121947440","https://openalex.org/W2127218421","https://openalex.org/W2145094598","https://openalex.org/W2163922914","https://openalex.org/W2173520492","https://openalex.org/W2187089797","https://openalex.org/W2212660284","https://openalex.org/W2299467264","https://openalex.org/W2533545350","https://openalex.org/W2559823555","https://openalex.org/W2593814746","https://openalex.org/W2608862709","https://openalex.org/W2741943936","https://openalex.org/W2750384547","https://openalex.org/W2753738274","https://openalex.org/W2779692282","https://openalex.org/W2785519580","https://openalex.org/W2799121188","https://openalex.org/W2883725317","https://openalex.org/W2904849495","https://openalex.org/W2906298613","https://openalex.org/W2950292946","https://openalex.org/W2951506174","https://openalex.org/W2953791858","https://openalex.org/W2962852342","https://openalex.org/W2962879692","https://openalex.org/W2963113011","https://openalex.org/W2963226019","https://openalex.org/W2963306805","https://openalex.org/W2963761396","https://openalex.org/W2963891243","https://openalex.org/W2964074409","https://openalex.org/W2964127395","https://openalex.org/W2990500698","https://openalex.org/W3005680577","https://openalex.org/W3088769475","https://openalex.org/W4212774754","https://openalex.org/W6640963894","https://openalex.org/W6678914141","https://openalex.org/W6685380521","https://openalex.org/W6688325169","https://openalex.org/W6718140377","https://openalex.org/W6728550200","https://openalex.org/W6735913928","https://openalex.org/W6744627333","https://openalex.org/W6748223763","https://openalex.org/W6748391871","https://openalex.org/W6751772990","https://openalex.org/W6757140150","https://openalex.org/W6780336448"],"related_works":["https://openalex.org/W2111119584","https://openalex.org/W2606552659","https://openalex.org/W4301002638","https://openalex.org/W3186815950","https://openalex.org/W2163563073","https://openalex.org/W3088133960","https://openalex.org/W2921708367","https://openalex.org/W2607902515","https://openalex.org/W2001483765","https://openalex.org/W2590117803"],"abstract_inverted_index":{"To":[0],"overcome":[1],"the":[2,46,49,61,66,76,84,87,91,96,122,132,136],"high":[3],"dimensionality":[4],"problem":[5],"of":[6,51,86,93],"data,":[7,71],"learning":[8],"feature":[9],"representations":[10],"for":[11],"clustering":[12,29,42,145],"has":[13],"been":[14],"widely":[15],"studied.":[16],"In":[17],"this":[18,81],"work,":[19],"we":[20],"propose":[21],"Disentangling":[22],"Latent":[23],"Space":[24],"Clustering":[25],"(DLS-Clustering),":[26],"a":[27],"new":[28],"framework":[30,82],"that":[31,131],"directly":[32,117],"learns":[33],"cluster":[34],"assignments":[35],"from":[36],"disentangled":[37],"latent":[38,102,111],"spacing":[39],"without":[40],"additional":[41],"methods.":[43,146],"We":[44,64],"enforce":[45],"encoder":[47,88],"and":[48,95,109,121,140],"generator":[50],"GAN":[52,94],"to":[53,60,89],"form":[54],"an":[55],"encoder-generator":[56,67],"pair":[57,68],"in":[58],"addition":[59],"generator-encoder":[62],"pair.":[63],"train":[65],"using":[69],"real":[70,77],"which":[72,100],"can":[73,115],"implicitly":[74],"estimate":[75],"conditional":[78],"distribution.":[79],"Meanwhile,":[80],"enforces":[83],"outputs":[85],"match":[90],"inputs":[92],"prior":[97],"noise":[98],"distribution,":[99],"disentangles":[101],"space":[103],"into":[104],"two":[105],"parts:":[106],"one-hot":[107],"discrete":[108],"continuous":[110],"variables.":[112],"The":[113],"former":[114],"be":[116],"expressed":[118],"as":[119],"clusters":[120],"latter":[123],"represents":[124],"remaining":[125],"unspecified":[126],"factors.":[127],"Our":[128],"experiments":[129],"show":[130],"proposed":[133],"method":[134],"achieves":[135],"optimal":[137],"disentanglement":[138],"performance":[139],"outperforms":[141],"existing":[142],"generative":[143],"model-based":[144]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}