{"id":"https://openalex.org/W2910918297","doi":"https://doi.org/10.1109/access.2019.2892074","title":"Partial Multi-View Clustering Based on Sparse Embedding Framework","display_name":"Partial Multi-View Clustering Based on Sparse Embedding Framework","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2910918297","doi":"https://doi.org/10.1109/access.2019.2892074","mag":"2910918297"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2892074","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2892074","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08610069.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08610069.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016914227","display_name":"Mingshu Ji","orcid":"https://orcid.org/0000-0002-1453-3778"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingshu Ji","raw_affiliation_strings":["Computer Center, Nanchang University, Nanchang, China"],"raw_orcid":"https://orcid.org/0000-0002-1453-3778","affiliations":[{"raw_affiliation_string":"Computer Center, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100813758","display_name":"Hong Rao","orcid":"https://orcid.org/0000-0002-7467-7108"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Rao","raw_affiliation_strings":["Computer Center, Nanchang University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Center, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045905018","display_name":"Zhixun Li","orcid":null},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixun Li","raw_affiliation_strings":["Computer Center, Nanchang University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Center, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025613458","display_name":"Jian Zhu","orcid":"https://orcid.org/0000-0001-7845-6348"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Zhu","raw_affiliation_strings":["Computer Center, Nanchang University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Center, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100744108","display_name":"Ning Wang","orcid":"https://orcid.org/0000-0002-8801-6255"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Wang","raw_affiliation_strings":["Computer Center, Nanchang University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Center, Nanchang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141649914"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9142,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.78492473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"29332","last_page":"29343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9972000122070312,"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":0.9972000122070312,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.98089998960495,"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":0.7988606691360474},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7209699153900146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7119638919830322},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5917278528213501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5641948580741882},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5439751744270325},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.49723461270332336},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.48093006014823914},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.46808940172195435},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4456930458545685},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4230373501777649},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4024386703968048},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.3968990445137024},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17483264207839966}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7988606691360474},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7209699153900146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7119638919830322},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5917278528213501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5641948580741882},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5439751744270325},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.49723461270332336},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.48093006014823914},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.46808940172195435},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4456930458545685},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4230373501777649},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4024386703968048},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.3968990445137024},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17483264207839966},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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":2,"locations":[{"id":"doi:10.1109/access.2019.2892074","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2892074","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08610069.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9a12f7e9659d41f3970a73723795c90a","is_oa":true,"landing_page_url":"https://doaj.org/article/9a12f7e9659d41f3970a73723795c90a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 29332-29343 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2892074","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2892074","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08610069.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6700000166893005,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2062929007","display_name":null,"funder_award_id":"20171BBE50063","funder_id":"https://openalex.org/F4320327780","funder_display_name":"Key Research and Development Program of Jiangxi Province"},{"id":"https://openalex.org/G3892108993","display_name":"\u57fa\u4e8e\u4e09\u5143\u7c97\u7cd9\u8f93\u51fa\u7f16\u7801\u7684\u5e26\u81ea\u9002\u5e94\u60e9\u7f5a\u56e0\u5b50\u7684\u652f\u6301\u5411\u91cf\u673a\u591a\u5206\u7c7b\u6a21\u578b\u7814\u7a76","funder_award_id":"61262047","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327780","display_name":"Key Research and Development Program of Jiangxi Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2910918297.pdf","grobid_xml":"https://content.openalex.org/works/W2910918297.grobid-xml"},"referenced_works_count":72,"referenced_works":["https://openalex.org/W33214042","https://openalex.org/W117482296","https://openalex.org/W129703402","https://openalex.org/W297461772","https://openalex.org/W1201875361","https://openalex.org/W1670132599","https://openalex.org/W1700224340","https://openalex.org/W1890834058","https://openalex.org/W1989529706","https://openalex.org/W1991311318","https://openalex.org/W1992405901","https://openalex.org/W1992905164","https://openalex.org/W1993962865","https://openalex.org/W2001141328","https://openalex.org/W2005876975","https://openalex.org/W2013029404","https://openalex.org/W2027461913","https://openalex.org/W2027805700","https://openalex.org/W2035746673","https://openalex.org/W2039361386","https://openalex.org/W2061918906","https://openalex.org/W2084716923","https://openalex.org/W2089571385","https://openalex.org/W2100556411","https://openalex.org/W2115429828","https://openalex.org/W2123921160","https://openalex.org/W2127271355","https://openalex.org/W2135046866","https://openalex.org/W2154189032","https://openalex.org/W2154415691","https://openalex.org/W2154872931","https://openalex.org/W2160547390","https://openalex.org/W2160616617","https://openalex.org/W2163463863","https://openalex.org/W2164071167","https://openalex.org/W2181159407","https://openalex.org/W2270614106","https://openalex.org/W2276595871","https://openalex.org/W2279323413","https://openalex.org/W2296399167","https://openalex.org/W2297991835","https://openalex.org/W2405459681","https://openalex.org/W2478889868","https://openalex.org/W2519051215","https://openalex.org/W2547190925","https://openalex.org/W2588822028","https://openalex.org/W2590019597","https://openalex.org/W2604120189","https://openalex.org/W2606067931","https://openalex.org/W2608812095","https://openalex.org/W2609701076","https://openalex.org/W2735834259","https://openalex.org/W2748391982","https://openalex.org/W2804557543","https://openalex.org/W2899604627","https://openalex.org/W2903938664","https://openalex.org/W2904934357","https://openalex.org/W2906529026","https://openalex.org/W3195117676","https://openalex.org/W6601318844","https://openalex.org/W6605295242","https://openalex.org/W6610472804","https://openalex.org/W6659319144","https://openalex.org/W6682644385","https://openalex.org/W6682991666","https://openalex.org/W6685423869","https://openalex.org/W6694420051","https://openalex.org/W6730107233","https://openalex.org/W6736969840","https://openalex.org/W6757172697","https://openalex.org/W6757202293","https://openalex.org/W6801005296"],"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/W4294224199"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,263],"novel":[6],"partial":[7,86,93,157],"multi-view":[8,51,158],"clustering":[9,27,46,52,131,159,251,265,271],"method":[10,188,261],"based":[11,101,160],"on":[12,92,102,161,211],"the":[13,110,116,140,150,219,230,233,238,244,254,259,268],"sparse":[14,162,182,215],"embedding":[15,163],"framework,":[16],"which":[17,38,97,142,168,217],"can":[18],"handle":[19],"incomplete":[20],"view":[21,72,94],"data":[22,75],"well":[23],"and":[24,121,171,184,206,224,243,253],"obtain":[25],"good":[26],"performance.":[28],"Most":[29],"real-world":[30],"datasets":[31],"are":[32,89,100,174,247],"often":[33,77],"comprised":[34],"of":[35,73,82,119,149,227],"different":[36],"views,":[37],"provide":[39],"complementary":[40],"information":[41,152,193],"for":[42,45],"each":[43,56,71],"other":[44,269],"or":[47,62],"classification":[48],"tasks.":[49],"Existing":[50],"methods":[53,132],"assume":[54],"that":[55,112,258],"example":[57],"appears":[58],"in":[59,85,96,153,167,196],"all":[60,63,99],"views":[61],"examples":[64],"contained":[65],"at":[66],"least":[67],"one":[68],"view.":[69],"But":[70],"real":[74],"is":[76,143,165],"subject":[78],"to":[79,108,145,179,203,249],"various":[80],"degrees":[81],"damage":[83],"resulting":[84],"examples.":[87],"There":[88],"some":[90],"works":[91],"clustering,":[95],"almost":[98],"standard":[103],"non-negative":[104],"matrix":[105,202],"factorization":[106],"(NMF)":[107],"solve":[109],"problem":[111],"did":[113],"not":[114],"consider":[115],"sparseness":[117],"degree":[118],"coefficient":[120],"unsatisfactory":[122],"basic":[123],"matrices":[124],"learned":[125,220],"by":[126,199],"NMF.":[127],"Besides,":[128],"most":[129],"existing":[130],"only":[133],"carry":[134],"out":[135],"dimensionality":[136,169],"reduction":[137,170],"before":[138],"learning":[139,173],"models,":[141],"unable":[144],"make":[146],"full":[147],"use":[148],"discriminative":[151],"raw":[154],"data.":[155],"Thus,":[156],"framework":[164],"proposed,":[166],"dictionary":[172],"taken":[175],"into":[176],"consideration":[177],"simultaneously":[178],"learn":[180],"better":[181,264],"coefficients":[183],"dictionaries.":[185],"The":[186],"proposed":[187,260],"preserves":[189],"as":[190,194],"much":[191],"useful":[192],"possible":[195],"original":[197],"space":[198],"constraining":[200],"projection":[201],"be":[204],"orthogonal":[205],"imposes":[207],"Fisher":[208],"discrimination":[209],"analysis":[210],"dictionaries":[212,221],"rather":[213],"than":[214,267],"coefficients,":[216],"makes":[218],"more":[222],"discriminating":[223],"promotes":[225],"sparsity":[226],"coefficients.":[228],"Finally,":[229],"synthetic":[231],"dataset,":[232,237],"extended":[234],"Yale":[235],"B":[236],"MNISIT":[239],"Handwritten":[240],"Digit":[241],"dataset":[242],"large":[245],"dataset-Caltech101":[246],"employed":[248],"perform":[250],"tasks,":[252],"experimental":[255],"results":[256],"show":[257],"achieved":[262],"performance":[266],"state-of-the-art":[270],"methods.":[272]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}