{"id":"https://openalex.org/W2972333410","doi":"https://doi.org/10.1109/lsp.2019.2941368","title":"Deep Clustering via Weighted $k$-Subspace Network","display_name":"Deep Clustering via Weighted $k$-Subspace Network","publication_year":2019,"publication_date":"2019-09-13","ids":{"openalex":"https://openalex.org/W2972333410","doi":"https://doi.org/10.1109/lsp.2019.2941368","mag":"2972333410"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2019.2941368","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2941368","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"},"type":"article","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/A5001203027","display_name":"Weitian Huang","orcid":"https://orcid.org/0000-0001-8848-4801"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weitian Huang","raw_affiliation_strings":["Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8848-4801","affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101993864","display_name":"Ming Yin","orcid":"https://orcid.org/0000-0002-7037-1048"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Yin","raw_affiliation_strings":["Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7037-1048","affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387063","display_name":"Jianzhong Li","orcid":"https://orcid.org/0000-0002-0226-4064"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzhong Li","raw_affiliation_strings":["Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0226-4064","affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043262479","display_name":"Shengli Xie","orcid":"https://orcid.org/0000-0003-2041-5214"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengli Xie","raw_affiliation_strings":["Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2041-5214","affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of IoT Information Processing, School of Automation, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":1.7866,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.88561953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"26","issue":"11","first_page":"1628","last_page":"1632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9983999729156494,"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":0.9983999729156494,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9976999759674072,"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/T10860","display_name":"Speech and Audio Processing","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/linear-subspace","display_name":"Linear subspace","score":0.7680761814117432},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.7273356914520264},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7208755016326904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6240519285202026},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.577501654624939},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5641458630561829},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5410737991333008},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5351057648658752},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.45868539810180664},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4259507656097412},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4129975736141205},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38615429401397705},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.26868295669555664}],"concepts":[{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.7680761814117432},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7273356914520264},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7208755016326904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6240519285202026},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.577501654624939},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5641458630561829},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5410737991333008},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5351057648658752},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.45868539810180664},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4259507656097412},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4129975736141205},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38615429401397705},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26868295669555664},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2019.2941368","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2941368","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7565494205","display_name":null,"funder_award_id":"2017A010101024","funder_id":"https://openalex.org/F4320335795","funder_display_name":"Science and Technology Planning Project of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320335795","display_name":"Science and Technology Planning Project of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533128638","https://openalex.org/W1959608418","https://openalex.org/W1995450389","https://openalex.org/W2049633694","https://openalex.org/W2076063813","https://openalex.org/W2110798204","https://openalex.org/W2118858274","https://openalex.org/W2121947440","https://openalex.org/W2127218421","https://openalex.org/W2128728535","https://openalex.org/W2154604839","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2222512263","https://openalex.org/W2509065397","https://openalex.org/W2533545350","https://openalex.org/W2579223288","https://openalex.org/W2608862709","https://openalex.org/W2618530766","https://openalex.org/W2730106296","https://openalex.org/W2747622561","https://openalex.org/W2750384547","https://openalex.org/W2751253323","https://openalex.org/W2751449619","https://openalex.org/W2765741717","https://openalex.org/W2788113664","https://openalex.org/W2794673239","https://openalex.org/W2884851420","https://openalex.org/W2889793054","https://openalex.org/W2908322374","https://openalex.org/W2922118332","https://openalex.org/W2953791858","https://openalex.org/W2963365397","https://openalex.org/W2963487393","https://openalex.org/W2964074409","https://openalex.org/W2964121744","https://openalex.org/W3098357269","https://openalex.org/W3106029105","https://openalex.org/W6631190155","https://openalex.org/W6640963894","https://openalex.org/W6676481782","https://openalex.org/W6678914141","https://openalex.org/W6685380521","https://openalex.org/W6728550200","https://openalex.org/W6732748622","https://openalex.org/W6743484365","https://openalex.org/W6743688258","https://openalex.org/W6744043827","https://openalex.org/W6748166593","https://openalex.org/W6755477582","https://openalex.org/W6762021997"],"related_works":["https://openalex.org/W3161902687","https://openalex.org/W2016708865","https://openalex.org/W2613362820","https://openalex.org/W2167091752","https://openalex.org/W4205559938","https://openalex.org/W3080831198","https://openalex.org/W3199679192","https://openalex.org/W4309316795","https://openalex.org/W2087414729","https://openalex.org/W1905452750"],"abstract_inverted_index":{"Subspace":[0],"clustering":[1,33,51,153,168],"aims":[2],"to":[3,27,36,52,78,102,113,133],"separate":[4],"the":[5,10,13,16,22,28,76,81,84,89,95,99,108,114,118,126,130,137,149,165],"data":[6,111],"into":[7,83,98],"clusters":[8],"under":[9],"hypothesis":[11],"that":[12,161],"samples":[14,82],"within":[15],"same":[17,23],"cluster":[18],"will":[19],"lie":[20],"in":[21,154],"low-dimensional":[24,85],"subspace.":[25,116],"Due":[26],"tough":[29],"pairwise":[30],"constraints,":[31],"k-subspace":[32,50,91],"is":[34],"sensitive":[35],"outliers":[37],"and":[38,69,152],"initialization.":[39],"In":[40,88],"this":[41,54],"letter,":[42],"we":[43,93,146],"present":[44],"a":[45,142,155],"novel":[46],"deep":[47],"architecture":[48],"for":[49],"address":[53],"issue,":[55],"called":[56],"as":[57,141],"Deep":[58],"Weighted":[59],"k-Subspace":[60],"Clustering":[61],"(DWSC).":[62],"Specifically,":[63],"our":[64,162],"framework":[65],"consists":[66],"of":[67,110,129],"autoencoder":[68,77],"weighted":[70,90],"k-subsapce":[71],"network.":[72],"We":[73],"first":[74],"use":[75],"non-linearly":[79],"compress":[80],"latent":[86,96,131],"space.":[87],"network,":[92],"feed":[94],"representation":[97,150],"assignment":[100],"network":[101],"output":[103],"soft":[104,139],"assignments":[105,140],"which":[106],"represent":[107],"probability":[109],"belonging":[112],"according":[115],"Subsequently,":[117],"optimal":[119],"k":[120],"subspaces":[121],"are":[122],"identified":[123],"by":[124],"minimizing":[125],"projection":[127],"residuals":[128],"representations":[132],"all":[134],"subspaces,":[135],"using":[136],"learned":[138],"weighting":[143],"vector.":[144],"Finally,":[145],"jointly":[147],"optimize":[148],"learning":[151],"unified":[156],"framework.":[157],"Experimental":[158],"results":[159],"show":[160],"approach":[163],"outperforms":[164],"state-of-the-art":[166],"subspace":[167],"methods":[169],"on":[170],"two":[171],"benchmark":[172],"datasets.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}