{"id":"https://openalex.org/W2792623581","doi":"https://doi.org/10.1109/spac.2017.8304247","title":"Discriminative multiview nonnegative matrix factorization with large margin for image classification","display_name":"Discriminative multiview nonnegative matrix factorization with large margin for image classification","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2792623581","doi":"https://doi.org/10.1109/spac.2017.8304247","mag":"2792623581"},"language":"en","primary_location":{"id":"doi:10.1109/spac.2017.8304247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spac.2017.8304247","pdf_url":null,"source":{"id":"https://openalex.org/S4306498208","display_name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","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/A5100759521","display_name":"Fei Long","orcid":"https://orcid.org/0009-0000-1553-2171"},"institutions":[{"id":"https://openalex.org/I4210156872","display_name":"Guizhou Institute of Technology","ror":"https://ror.org/05x510r30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156872"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fei Long","raw_affiliation_strings":["School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China","institution_ids":["https://openalex.org/I4210156872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028245257","display_name":"Weihua Ou","orcid":"https://orcid.org/0000-0001-5241-7703"},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihua Ou","raw_affiliation_strings":["School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016164542","display_name":"Kesheng Zhang","orcid":"https://orcid.org/0000-0003-3910-1581"},"institutions":[{"id":"https://openalex.org/I4210156872","display_name":"Guizhou Institute of Technology","ror":"https://ror.org/05x510r30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kesheng Zhang","raw_affiliation_strings":["School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China","institution_ids":["https://openalex.org/I4210156872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102242976","display_name":"Yi Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156872","display_name":"Guizhou Institute of Technology","ror":"https://ror.org/05x510r30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Tan","raw_affiliation_strings":["School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China","institution_ids":["https://openalex.org/I4210156872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018463804","display_name":"Yunhao Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156872","display_name":"Guizhou Institute of Technology","ror":"https://ror.org/05x510r30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhao Xue","raw_affiliation_strings":["School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Guizhou Institute of Technology, Guiyang, China","institution_ids":["https://openalex.org/I4210156872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071147910","display_name":"Gai Li","orcid":"https://orcid.org/0000-0002-4340-0413"},"institutions":[{"id":"https://openalex.org/I4210120302","display_name":"Shunde Polytechnic","ror":"https://ror.org/01w9d4603","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210120302"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gai Li","raw_affiliation_strings":["Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Information Engineering, Shunde Polytechnic, Foshan, China","institution_ids":["https://openalex.org/I4210120302"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100759521"],"corresponding_institution_ids":["https://openalex.org/I4210156872"],"apc_list":null,"apc_paid":null,"fwci":0.0985,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48057971,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"28","issue":null,"first_page":"37","last_page":"42"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9980000257492065,"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":0.9980000257492065,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9977999925613403,"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/T10531","display_name":"Advanced Vision and Imaging","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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8351382613182068},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7200042605400085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6959456205368042},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6518555879592896},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6024652719497681},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5810208320617676},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5768299698829651},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5701478719711304},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5218154788017273},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4977281391620636},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.4534970223903656},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43205708265304565},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40425363183021545},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4021889567375183},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3745992183685303},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31183725595474243}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8351382613182068},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7200042605400085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6959456205368042},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6518555879592896},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6024652719497681},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5810208320617676},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5768299698829651},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5701478719711304},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5218154788017273},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4977281391620636},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.4534970223903656},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43205708265304565},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40425363183021545},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4021889567375183},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3745992183685303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31183725595474243},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spac.2017.8304247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spac.2017.8304247","pdf_url":null,"source":{"id":"https://openalex.org/S4306498208","display_name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1550054889","https://openalex.org/W1603575269","https://openalex.org/W1670132599","https://openalex.org/W1678657513","https://openalex.org/W1700224340","https://openalex.org/W1948068958","https://openalex.org/W1967722387","https://openalex.org/W1968580628","https://openalex.org/W1974573780","https://openalex.org/W1986817851","https://openalex.org/W2004346241","https://openalex.org/W2012150114","https://openalex.org/W2013029404","https://openalex.org/W2031326491","https://openalex.org/W2035299679","https://openalex.org/W2071207147","https://openalex.org/W2080744942","https://openalex.org/W2100235303","https://openalex.org/W2109857912","https://openalex.org/W2120455655","https://openalex.org/W2120865781","https://openalex.org/W2135160607","https://openalex.org/W2137823674","https://openalex.org/W2141566872","https://openalex.org/W2165685007","https://openalex.org/W2186500555","https://openalex.org/W2296319761","https://openalex.org/W2298605637","https://openalex.org/W2315792732","https://openalex.org/W2327699058","https://openalex.org/W2400262625","https://openalex.org/W2405459681","https://openalex.org/W2405705173","https://openalex.org/W2435477720","https://openalex.org/W2509359898","https://openalex.org/W2529748059","https://openalex.org/W2586138702","https://openalex.org/W2590019597","https://openalex.org/W2626280777","https://openalex.org/W2703881267","https://openalex.org/W2725281289","https://openalex.org/W4250589301","https://openalex.org/W4285719527","https://openalex.org/W6636883489","https://openalex.org/W6677937869","https://openalex.org/W6718044918"],"related_works":["https://openalex.org/W4390394189","https://openalex.org/W2149305241","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2902260311","https://openalex.org/W2045265907","https://openalex.org/W2972997031"],"abstract_inverted_index":{"Image":[0],"classification":[1,12,49],"has":[2],"attracted":[3],"lots":[4],"of":[5,23,32,78,104],"attentions":[6],"in":[7],"recent":[8],"years.":[9],"To":[10,35],"improve":[11],"accuracy,":[13],"multiple":[14],"features":[15,56],"are":[16,57,136],"usually":[17],"extracted":[18],"to":[19,70,112],"represent":[20],"the":[21,30,52,92,102,114,127,133],"context":[22],"images,":[24],"which":[25],"imposes":[26],"a":[27,41,76],"challenge":[28],"for":[29,47,101],"combination":[31],"those":[33,55],"features.":[34],"address":[36],"this":[37],"problem,":[38],"we":[39,62],"present":[40],"discriminative":[42,72],"nonnegative":[43,79],"multi-view":[44],"learning":[45],"approach":[46],"image":[48],"based":[50],"on":[51,91],"observation":[53],"that":[54],"often":[58],"nonnegative.":[59],"For":[60],"discrimination,":[61],"utilize":[63],"class":[64],"label":[65],"as":[66],"an":[67,119],"auxiliary":[68],"information":[69],"learn":[71],"common":[73,129],"representations":[74,94,131],"through":[75],"set":[77],"basis":[80],"vectors":[81],"with":[82],"large":[83],"margin.":[84],"Meanwhile,":[85],"view":[86],"consistency":[87],"constraint":[88],"is":[89,99],"imposed":[90],"low-dimensional":[93],"and":[95,117],"correntropy-induced":[96],"metric":[97],"(CIM)":[98],"adopted":[100],"measurement":[103],"reconstruction":[105],"errors.":[106],"We":[107],"utilized":[108],"half-quadratic":[109],"optimization":[110,115],"technique":[111],"solve":[113],"problem":[116],"obtain":[118],"effective":[120],"multiplicative":[121],"update":[122],"rule.":[123],"Experimental":[124],"results":[125],"demonstrate":[126],"learned":[128],"latent":[130],"by":[132],"proposed":[134],"method":[135],"more":[137],"efficient":[138],"than":[139],"other":[140],"methods.":[141]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}