{"id":"https://openalex.org/W3135064676","doi":"https://doi.org/10.3233/ida-195075","title":"Deep multiple non-negative matrix factorization for multi-view clustering","display_name":"Deep multiple non-negative matrix factorization for multi-view clustering","publication_year":2021,"publication_date":"2021-03-04","ids":{"openalex":"https://openalex.org/W3135064676","doi":"https://doi.org/10.3233/ida-195075","mag":"3135064676"},"language":"en","primary_location":{"id":"doi:10.3233/ida-195075","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-195075","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","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/A5087560192","display_name":"Guowang Du","orcid":"https://orcid.org/0000-0002-8109-7152"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guowang Du","raw_affiliation_strings":["School of Information, Yunnan University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Yunnan University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100653521","display_name":"Lihua Zhou","orcid":"https://orcid.org/0000-0002-8940-1155"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lihua Zhou","raw_affiliation_strings":["School of Information, Yunnan University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Yunnan University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036548399","display_name":"Kevin L\u00fc","orcid":"https://orcid.org/0000-0002-2588-9059"},"institutions":[{"id":"https://openalex.org/I59433898","display_name":"Brunel University of London","ror":"https://ror.org/00dn4t376","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I59433898"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kevin L\u00fc","raw_affiliation_strings":["Brunel University, Uxbridge, UK"],"affiliations":[{"raw_affiliation_string":"Brunel University, Uxbridge, UK","institution_ids":["https://openalex.org/I59433898"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086339662","display_name":"Hanyi Ding","orcid":"https://orcid.org/0000-0002-0307-9119"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyan Ding","raw_affiliation_strings":["School of Information, Yunnan University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Yunnan University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100653521"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.8646,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.74803922,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"25","issue":"2","first_page":"339","last_page":"357"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9940999746322632,"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":0.9940999746322632,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.993399977684021,"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":0.9782000184059143,"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":0.7410356402397156},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7243497371673584},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6791818141937256},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6737344264984131},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5783228278160095},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5307642817497253},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5134159326553345},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5007603168487549},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46245479583740234},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.45933997631073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4463099539279938},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4260960817337036},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.41622138023376465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3840678334236145},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35243576765060425},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.333965927362442},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27936863899230957}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7410356402397156},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243497371673584},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6791818141937256},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6737344264984131},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5783228278160095},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5307642817497253},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5134159326553345},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5007603168487549},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46245479583740234},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45933997631073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4463099539279938},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4260960817337036},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.41622138023376465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3840678334236145},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35243576765060425},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.333965927362442},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27936863899230957},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"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:10.3233/ida-195075","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-195075","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1504886279","https://openalex.org/W1902027874","https://openalex.org/W2013029404","https://openalex.org/W2048679005","https://openalex.org/W2087205276","https://openalex.org/W2108119513","https://openalex.org/W2108433027","https://openalex.org/W2136922672","https://openalex.org/W2142674578","https://openalex.org/W2154415691","https://openalex.org/W2171209182","https://openalex.org/W2187089797","https://openalex.org/W2296531836","https://openalex.org/W2344657534","https://openalex.org/W2405459681","https://openalex.org/W2584959193","https://openalex.org/W2587115404","https://openalex.org/W2604132847","https://openalex.org/W2605146283","https://openalex.org/W2742098698","https://openalex.org/W2767834929","https://openalex.org/W2770558925","https://openalex.org/W2770642030","https://openalex.org/W2807957280","https://openalex.org/W2808000122","https://openalex.org/W2897979119","https://openalex.org/W2898541610","https://openalex.org/W2945444329","https://openalex.org/W2953752256","https://openalex.org/W2963166639","https://openalex.org/W6600728650"],"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/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W34555840"],"abstract_inverted_index":{"Multi-view":[0],"clustering":[1,34,202],"aims":[2],"to":[3,32,36,102,128,148,204],"group":[4],"similar":[5],"samples":[6,13],"into":[7,14,124],"the":[8,49,105,113,136,150,159,169,189,193],"same":[9],"clusters":[10,16],"and":[11,55,86,97,116,119,177,188],"dissimilar":[12],"different":[15],"by":[17],"integrating":[18],"heterogeneous":[19,56,137],"information":[20,57,138,153],"from":[21,108],"multi-view":[22,33,45,59,78,140,201],"data.":[23,60,141],"Non-negative":[24],"matrix":[25,70],"factorization":[26,71],"(NMF)":[27],"has":[28],"been":[29,186],"widely":[30],"applied":[31],"owing":[35],"its":[37],"interpretability.":[38],"However,":[39],"most":[40],"NMF-based":[41],"algorithms":[42],"only":[43],"factorize":[44,104],"data":[46,107],"based":[47,74],"on":[48,75,181],"shallow":[50],"structure,":[51],"neglecting":[52],"complex":[53],"hierarchical":[54,114],"in":[58],"In":[61],"this":[62],"paper,":[63],"we":[64],"propose":[65],"a":[66,109,130],"deep":[67,90],"multiple":[68,83],"non-negative":[69],"(DMNMF)":[72],"framework":[73],"AutoEncoder":[76],"for":[77,111,134,200],"clustering.":[79],"DMNMF":[80],"consists":[81],"of":[82,94,154,196],"Encoder":[84,95,118],"Components":[85,88,121],"Decoder":[87,98,120],"with":[89],"structures.":[91],"Each":[92],"pair":[93],"Component":[96,99],"are":[100,122,145],"used":[101],"hierarchically":[103],"input":[106],"view":[110],"capturing":[112],"information,":[115],"all":[117],"integrated":[123],"an":[125,162],"abstract":[126],"level":[127],"learn":[129],"common":[131],"low-dimensional":[132],"representation":[133],"combining":[135],"across":[139],"Furthermore,":[142],"graph":[143],"regularizers":[144],"also":[146,175],"introduced":[147],"preserve":[149],"local":[151],"geometric":[152],"each":[155],"view.":[156],"To":[157],"optimize":[158],"proposed":[160,176,198],"framework,":[161],"iterative":[163],"updating":[164],"scheme":[165],"is":[166,174],"developed.":[167],"Besides,":[168],"corresponding":[170],"algorithm":[171],"called":[172],"MVC-DMNMF":[173,199],"implemented.":[178],"Extensive":[179],"experiments":[180],"six":[182],"benchmark":[183],"datasets":[184],"have":[185],"conducted,":[187],"experimental":[190],"results":[191],"demonstrate":[192],"superior":[194],"performance":[195],"our":[197],"compared":[203],"other":[205],"baseline":[206],"algorithms.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
