{"id":"https://openalex.org/W4224931692","doi":"https://doi.org/10.1109/icassp43922.2022.9747734","title":"Discrete Multi-Kernel K-Means with Diverse and Optimal Kernel Learning","display_name":"Discrete Multi-Kernel K-Means with Diverse and Optimal Kernel Learning","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224931692","doi":"https://doi.org/10.1109/icassp43922.2022.9747734"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747734","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5056893045","display_name":"Yihang Lu","orcid":"https://orcid.org/0000-0001-7322-8602"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]},{"id":"https://openalex.org/I890469752","display_name":"Ministry of Industry and Information Technology","ror":"https://ror.org/0385nmy68","country_code":"CN","type":"government","lineage":["https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yihang Lu","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,Xi’an,P. R. China,710072","School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, P. R. China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,Xi’an,P. R. China,710072","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I890469752","https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020877437","display_name":"Jitao Lu","orcid":"https://orcid.org/0000-0002-6065-2639"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]},{"id":"https://openalex.org/I890469752","display_name":"Ministry of Industry and Information Technology","ror":"https://ror.org/0385nmy68","country_code":"CN","type":"government","lineage":["https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jitao Lu","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,Xi’an,P. R. China,710072","School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, P. R. China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,Xi’an,P. R. China,710072","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I890469752","https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030351224","display_name":"Rong Wang","orcid":"https://orcid.org/0000-0001-9240-6726"},"institutions":[{"id":"https://openalex.org/I890469752","display_name":"Ministry of Industry and Information Technology","ror":"https://ror.org/0385nmy68","country_code":"CN","type":"government","lineage":["https://openalex.org/I890469752"]},{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Wang","raw_affiliation_strings":["Northwestern Polytechnical University,School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology),Xi’an,P. R. China,710072"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology),Xi’an,P. R. China,710072","institution_ids":["https://openalex.org/I890469752","https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003222421","display_name":"Feiping Nie","orcid":"https://orcid.org/0000-0002-0871-6519"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]},{"id":"https://openalex.org/I890469752","display_name":"Ministry of Industry and Information Technology","ror":"https://ror.org/0385nmy68","country_code":"CN","type":"government","lineage":["https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiping Nie","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,Xi’an,P. R. China,710072","School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, P. R. China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,Xi’an,P. R. China,710072","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an, P. R. China","institution_ids":["https://openalex.org/I890469752","https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5056893045"],"corresponding_institution_ids":["https://openalex.org/I17145004","https://openalex.org/I890469752"],"apc_list":null,"apc_paid":null,"fwci":0.6597,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.77684301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4153","last_page":"4157"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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/T12676","display_name":"Machine Learning and ELM","score":0.9976999759674072,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9969000220298767,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7744874954223633},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.7337478399276733},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.7043527960777283},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.6270424127578735},{"id":"https://openalex.org/keywords/string-kernel","display_name":"String kernel","score":0.626589298248291},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5961989760398865},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.56766277551651},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5527092218399048},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5432894825935364},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.5260170102119446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5061976313591003},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5015826225280762},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45041438937187195},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36858922243118286},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32242828607559204},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12320861220359802},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.09126046299934387}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7744874954223633},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.7337478399276733},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.7043527960777283},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.6270424127578735},{"id":"https://openalex.org/C55851704","wikidata":"https://www.wikidata.org/wiki/Q7623983","display_name":"String kernel","level":5,"score":0.626589298248291},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5961989760398865},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.56766277551651},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5527092218399048},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5432894825935364},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.5260170102119446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5061976313591003},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5015826225280762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45041438937187195},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36858922243118286},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32242828607559204},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12320861220359802},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.09126046299934387}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747734","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W53387266","https://openalex.org/W62192748","https://openalex.org/W303668437","https://openalex.org/W1977556410","https://openalex.org/W1979089718","https://openalex.org/W1986007546","https://openalex.org/W2000769684","https://openalex.org/W2109743529","https://openalex.org/W2132205911","https://openalex.org/W2140095548","https://openalex.org/W2171571997","https://openalex.org/W2560185252","https://openalex.org/W2569859441","https://openalex.org/W2576553562","https://openalex.org/W2604114154","https://openalex.org/W2740420891","https://openalex.org/W2899272023","https://openalex.org/W2908035133","https://openalex.org/W2912580424","https://openalex.org/W2945696115","https://openalex.org/W3008561560","https://openalex.org/W3120740533","https://openalex.org/W3190216563","https://openalex.org/W4237593694","https://openalex.org/W4288006479","https://openalex.org/W6644682428","https://openalex.org/W6685124330","https://openalex.org/W6732346129","https://openalex.org/W6756042798","https://openalex.org/W6770261863","https://openalex.org/W6774220812"],"related_works":["https://openalex.org/W3100948281","https://openalex.org/W4291669689","https://openalex.org/W2071590642","https://openalex.org/W1983263273","https://openalex.org/W1969447452","https://openalex.org/W1535136526","https://openalex.org/W2371064519","https://openalex.org/W2806943235","https://openalex.org/W2970160020","https://openalex.org/W2092483655"],"abstract_inverted_index":{"Multiple":[0],"Kernel":[1,82],"k-means":[2,77],"and":[3,35,43,80,100,105,123,132],"its":[4],"variants":[5],"integrate":[6],"a":[7,62,90],"group":[8],"of":[9,40,155],"kernels":[10,25,50,114],"to":[11,26,52,66,115],"improve":[12],"clustering":[13],"performance,":[14],"but":[15],"it":[16,108,128],"still":[17],"has":[18],"some":[19],"drawbacks:":[20],"1)":[21],"linearly":[22],"combining":[23],"base":[24,97],"get":[27],"the":[28,32,38,47,74,96,102,111,117,136,153],"optimal":[29],"one":[30],"limits":[31],"kernel":[33,41,53,92,98,103,118],"representability":[34],"cuts":[36],"off":[37],"negotiation":[39],"learning":[42,104],"clustering;":[44],"2)":[45],"ignoring":[46],"correlation":[48],"among":[49],"leads":[51,65],"redundancy;":[54],"3)":[55],"solving":[56],"NP-hard":[57],"cluster":[58],"assignment":[59],"problem":[60],"by":[61,93],"two-stage":[63],"strategy":[64],"information":[67,143],"loss.":[68,144],"In":[69],"this":[70],"paper,":[71],"we":[72],"propose":[73],"Discrete":[75],"Multi-kernel":[76],"with":[78,120],"Diverse":[79],"Optimal":[81],"Learning":[83],"(DMK-DOK)":[84],"model,":[85],"which":[86,140],"adaptively":[87],"seeks":[88],"for":[89],"better":[91],"residing":[94],"in":[95,135],"neighborhood":[99],"negotiates":[101],"clustering.":[106],"Moreover,":[107],"implicitly":[109],"penalizes":[110],"highly":[112],"correlated":[113],"enhance":[116],"fusion":[119],"less":[121],"redundancy":[122],"more":[124],"diversity.":[125],"What\u2019s":[126],"more,":[127],"jointly":[129],"learns":[130],"discrete":[131],"relaxed":[133],"labels":[134],"same":[137],"optimization":[138],"objective,":[139],"can":[141],"avoid":[142],"Lastly,":[145],"extensive":[146],"experiments":[147],"conducted":[148],"on":[149],"real-world":[150],"datasets":[151],"illustrated":[152],"superiority":[154],"our":[156],"model.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}