{"id":"https://openalex.org/W2902820091","doi":"https://doi.org/10.1109/icpr.2018.8546021","title":"Nonnegative and Adaptive Multi-view Clustering","display_name":"Nonnegative and Adaptive Multi-view Clustering","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2902820091","doi":"https://doi.org/10.1109/icpr.2018.8546021","mag":"2902820091"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8546021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546021","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5010898755","display_name":"Peng Zou","orcid":"https://orcid.org/0000-0002-8484-5769"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Zou","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013713640","display_name":"Fanzhang Li","orcid":"https://orcid.org/0000-0003-4318-3081"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanzhang Li","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100425448","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-7914-0679"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010898755"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.2089,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.56927348,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1247","last_page":"1252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987000226974487,"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":0.9987000226974487,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9934999942779541,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9925000071525574,"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/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.8086268901824951},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7205418944358826},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5959675312042236},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5786100029945374},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5616759657859802},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.48704007267951965},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48662814497947693},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42148858308792114},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.41526469588279724},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1493133008480072},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.09759268164634705}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.8086268901824951},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7205418944358826},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5959675312042236},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5786100029945374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5616759657859802},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.48704007267951965},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48662814497947693},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42148858308792114},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.41526469588279724},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1493133008480072},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.09759268164634705},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/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/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/icpr.2018.8546021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546021","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1700224340","https://openalex.org/W1975172027","https://openalex.org/W1977556410","https://openalex.org/W1978259121","https://openalex.org/W1983293338","https://openalex.org/W2009501510","https://openalex.org/W2053186076","https://openalex.org/W2076363162","https://openalex.org/W2101324110","https://openalex.org/W2108119513","https://openalex.org/W2123576058","https://openalex.org/W2135029798","https://openalex.org/W2142109962","https://openalex.org/W2142674578","https://openalex.org/W2154415691","https://openalex.org/W2166049352","https://openalex.org/W2166782149","https://openalex.org/W2199534117","https://openalex.org/W2405459681","https://openalex.org/W2571268788","https://openalex.org/W2740464254","https://openalex.org/W3120740533","https://openalex.org/W3143596294","https://openalex.org/W6637424748","https://openalex.org/W6644682428","https://openalex.org/W6646284504","https://openalex.org/W6675134712","https://openalex.org/W6680012447","https://openalex.org/W6682991666"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2972997031","https://openalex.org/W2045265907","https://openalex.org/W2146544734"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3],"novel":[4],"Nonnegative":[5],"and":[6,22,40,50],"Adaptive":[7],"Multi-view":[8],"Clustering":[9],"(NAMC)":[10],"method.":[11],"NAMC":[12,30,112,137],"integrates":[13],"the":[14,32,37,41,64,69,75,83,89,100,110,117],"nonnegative":[15,33,53,101],"matrix":[16,26,93,104],"factorization":[17],"(NMF),":[18],"adaptive":[19,24,54,84,102],"neighborhood":[20,85],"learning":[21,35],"consensus":[23,91],"similarity":[25,92,103],"fusion.":[27],"More":[28],"specifically,":[29],"performs":[31],"weight":[34],"over":[36],"original":[38,70],"data":[39],"parts-based":[42,66,79],"representations":[43],"of":[44,68,78,105],"NMF":[45,61],"for":[46,146],"more":[47],"accurate":[48],"measure":[49],"representation.":[51],"For":[52],"feature":[55],"extraction,":[56],"our":[57,123],"model":[58],"first":[59],"utilizes":[60],"to":[62,99],"obtain":[63],"local":[65,76],"representation":[67],"high-dimensional":[71],"data.":[72],"To":[73],"keep":[74],"structure":[77],"representations,":[80],"we":[81],"minimize":[82],"reconstruction":[86],"error.":[87],"Then":[88],"optimal":[90],"can":[94,126,138],"be":[95,127],"iteratively":[96],"obtained":[97,121],"according":[98],"each":[106],"view.":[107],"What's":[108],"more,":[109],"proposed":[111],"is":[113,120],"totally":[114],"self-weighted.":[115],"Once":[116],"target":[118],"graph":[119],"in":[122],"model,":[124],"it":[125],"partitioned":[128],"into":[129],"specific":[130],"clusters":[131],"directly.":[132],"Extensive":[133],"simulations":[134],"show":[135],"that":[136],"achieve":[139],"good":[140],"performance":[141],"on":[142],"several":[143],"public":[144],"databases":[145],"multi-view":[147],"clustering,":[148],"compared":[149],"with":[150],"other":[151],"related":[152],"methods.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
