{"id":"https://openalex.org/W3113671571","doi":"https://doi.org/10.23919/eusipco47968.2020.9287350","title":"Graph Regularized Subspace Clustering via Low-Rank Decomposition","display_name":"Graph Regularized Subspace Clustering via Low-Rank Decomposition","publication_year":2020,"publication_date":"2020-12-18","ids":{"openalex":"https://openalex.org/W3113671571","doi":"https://doi.org/10.23919/eusipco47968.2020.9287350","mag":"3113671571"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco47968.2020.9287350","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-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/A5028119374","display_name":"Aimin Jiang","orcid":"https://orcid.org/0000-0002-9181-934X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aimin Jiang","raw_affiliation_strings":["College of Internet of Things Engineering, Hohai University, Changhzou, China"],"affiliations":[{"raw_affiliation_string":"College of Internet of Things Engineering, Hohai University, Changhzou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036031997","display_name":"Weigao Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weigao Cheng","raw_affiliation_strings":["College of Internet of Things Engineering, Hohai University, Changhzou, China"],"affiliations":[{"raw_affiliation_string":"College of Internet of Things Engineering, Hohai University, Changhzou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103209453","display_name":"Jing Shang","orcid":"https://orcid.org/0000-0003-4304-0355"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Shang","raw_affiliation_strings":["College of Internet of Things Engineering, Hohai University, Changhzou, China"],"affiliations":[{"raw_affiliation_string":"College of Internet of Things Engineering, Hohai University, Changhzou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000461979","display_name":"Xiaoyu Miao","orcid":"https://orcid.org/0000-0002-9809-9824"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Miao","raw_affiliation_strings":["College of Internet of Things Engineering, Hohai University, Changhzou, China"],"affiliations":[{"raw_affiliation_string":"College of Internet of Things Engineering, Hohai University, Changhzou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082927370","display_name":"Yanping Zhu","orcid":"https://orcid.org/0000-0002-8107-0101"},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanping Zhu","raw_affiliation_strings":["School of Information Science and Engineering, Changzhou University, Changzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028119374"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42718626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2165","last_page":"2169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9973000288009644,"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":0.9973000288009644,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9731000065803528,"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":0.622431755065918},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5804235935211182},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.578834593296051},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5710304379463196},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5453110337257385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5388109087944031},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.47090864181518555},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.4231593608856201},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3681671917438507},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36674964427948},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.353284627199173},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3468065559864044},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32906800508499146},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.10711219906806946}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.622431755065918},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5804235935211182},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.578834593296051},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5710304379463196},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5453110337257385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5388109087944031},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.47090864181518555},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.4231593608856201},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3681671917438507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36674964427948},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.353284627199173},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3468065559864044},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32906800508499146},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.10711219906806946},{"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:10.23919/eusipco47968.2020.9287350","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W191001584","https://openalex.org/W1600471557","https://openalex.org/W1979089718","https://openalex.org/W1993962865","https://openalex.org/W1997201895","https://openalex.org/W2006533296","https://openalex.org/W2075632264","https://openalex.org/W2100659887","https://openalex.org/W2118154608","https://openalex.org/W2125874614","https://openalex.org/W2132914434","https://openalex.org/W2140245639","https://openalex.org/W2151155403","https://openalex.org/W2253163421","https://openalex.org/W2500665848","https://openalex.org/W2577472518","https://openalex.org/W2789696179","https://openalex.org/W2798585159","https://openalex.org/W2802754347","https://openalex.org/W2963384510","https://openalex.org/W2994340921","https://openalex.org/W3100282875","https://openalex.org/W6635552349","https://openalex.org/W6691615350","https://openalex.org/W6731795620"],"related_works":["https://openalex.org/W2972477132","https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W3038102983","https://openalex.org/W2950907416","https://openalex.org/W1559483280","https://openalex.org/W2082479932"],"abstract_inverted_index":{"Subspace":[0],"clustering":[1,62],"(SC)":[2],"is":[3,94,109],"able":[4],"to":[5,18,96,112],"identify":[6],"low-dimensional":[7],"subspace":[8],"structures":[9],"embedded":[10],"in":[11,35],"high-dimensional":[12],"data.":[13],"Recently,":[14],"graph-regularized":[15],"approaches":[16,41],"aim":[17],"tackle":[19,113],"this":[20,65],"problem":[21],"by":[22],"learning":[23,79],"a":[24,32,36,43,69,87],"linear":[25],"representation":[26,49,92],"of":[27,90,106,131],"data":[28,81,123],"samples":[29],"and":[30,57,77,124],"also":[31],"graph":[33,44,55,78,84,99],"structure":[34,56],"unified":[37],"framework.":[38],"However,":[39],"previous":[40],"exploit":[42],"embedding":[45,100],"term":[46],"based":[47,72],"on":[48,73,120],"matrix,":[50],"which":[51],"could":[52],"over-smooth":[53],"the":[54,61,91,98,114,129,132],"thus":[58],"adversely":[59],"affect":[60],"performance.":[63],"In":[64,83],"paper,":[66],"we":[67],"present":[68],"novel":[70],"algorithm":[71],"joint":[74],"low-rank":[75,88],"decomposition":[76],"from":[80],"samples.":[82],"learning,":[85],"only":[86],"component":[89],"matrix":[93],"employed":[95],"construct":[97],"term.":[101],"An":[102],"alternating":[103],"direction":[104],"method":[105],"multipliers":[107],"(ADMM)":[108],"further":[110],"developed":[111],"resulting":[115],"nonconvex":[116],"problem.":[117],"Experimental":[118],"results":[119],"both":[121],"synthetic":[122],"real":[125],"benchmark":[126],"databases":[127],"validate":[128],"effectiveness":[130],"proposed":[133],"SC":[134],"algorithm.":[135]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}