{"id":"https://openalex.org/W4396629989","doi":"https://doi.org/10.1145/3654446.3654465","title":"Distributed Multi-kernel Learning Based on Gaussian Mixture Model with Missing Data","display_name":"Distributed Multi-kernel Learning Based on Gaussian Mixture Model with Missing Data","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4396629989","doi":"https://doi.org/10.1145/3654446.3654465"},"language":"en","primary_location":{"id":"doi:10.1145/3654446.3654465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654446.3654465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications","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/A5041330413","display_name":"Sicong Chen","orcid":"https://orcid.org/0009-0002-5744-2953"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sicong Chen","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, China"],"raw_orcid":"https://orcid.org/0009-0002-5744-2953","affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100414124","display_name":"Ying Liu","orcid":"https://orcid.org/0000-0002-0337-277X"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Liu","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, China"],"raw_orcid":"https://orcid.org/0000-0002-0337-277X","affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1632,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60661097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"106","last_page":"111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9979000091552734,"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/T10057","display_name":"Face and Expression Recognition","score":0.989300012588501,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9864000082015991,"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/computer-science","display_name":"Computer science","score":0.7562593221664429},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7410252094268799},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7152179479598999},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6680563688278198},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6098284721374512},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.605354368686676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5414140224456787},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4997138977050781},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.49365171790122986},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4893462061882019},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4353369474411011},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42633721232414246},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41171568632125854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1117391288280487}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7562593221664429},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7410252094268799},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7152179479598999},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6680563688278198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6098284721374512},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.605354368686676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5414140224456787},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4997138977050781},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.49365171790122986},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4893462061882019},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4353369474411011},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42633721232414246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41171568632125854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1117391288280487},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3654446.3654465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654446.3654465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2584022624","https://openalex.org/W2604114154","https://openalex.org/W2783367877","https://openalex.org/W2883916659","https://openalex.org/W2891713324","https://openalex.org/W2944884576","https://openalex.org/W2985474597","https://openalex.org/W2996075766","https://openalex.org/W3018058094","https://openalex.org/W3075060036","https://openalex.org/W3101919424","https://openalex.org/W3124197751","https://openalex.org/W3173854166","https://openalex.org/W3208554304","https://openalex.org/W4224931692","https://openalex.org/W4229025315","https://openalex.org/W4387872955"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Distributed":[0],"classification,":[1],"as":[2],"an":[3],"important":[4],"distributed":[5,68,81,90,110],"learning":[6],"task,":[7],"has":[8],"received":[9],"much":[10],"attention":[11],"for":[12,76,104],"more":[13],"than":[14],"two":[15],"decades.":[16],"However,":[17],"it":[18],"is":[19,99,116],"still":[20],"a":[21,67,83,89,109],"challenge":[22],"to":[23,48,101,118],"achieve":[24,119],"acceptable":[25],"classification":[26,77,120,144],"performance":[27,57,145],"on":[28,94,122,131],"some":[29,41,132],"real":[30],"datasets":[31],"that":[32],"follow":[33],"complex":[34],"distributions.":[35],"Moreover,":[36],"in":[37,62],"various":[38],"practical":[39],"scenarios,":[40],"data":[42,80],"features":[43],"are":[44],"unavoidably":[45],"missing":[46,79,106,151],"due":[47],"environmental":[49],"interference":[50],"or":[51],"human":[52],"factors,":[53],"which":[54],"affect":[55],"the":[56,86,105,123,138],"of":[58,78],"classification.":[59],"Considering":[60],"this,":[61],"this":[63],"paper,":[64],"we":[65,127],"propose":[66],"Gaussian":[69,95],"Mixture":[70,96],"Model-based":[71],"Multi-Kernel":[72],"Learning":[73],"(dGMM-MKL)":[74],"algorithm":[75,141],"over":[82],"network.":[84],"In":[85],"proposed":[87,139],"algorithm,":[88],"imputation":[91,103],"method":[92],"based":[93,121],"Model":[97],"(GMM)":[98],"used":[100],"make":[102],"components.":[107],"Then,":[108],"multi-kernel":[111],"support":[112],"vector":[113],"machine":[114],"(SVM)":[115],"developed":[117],"imputed":[124],"data.":[125],"Finally,":[126],"present":[128],"experiment":[129],"results":[130],"datasets.":[133],"Compared":[134],"with":[135],"state-of-the-art":[136],"algorithms,":[137],"dGMM-MKL":[140],"shows":[142],"better":[143],"and":[146],"higher":[147],"robustness":[148],"under":[149],"different":[150],"probabilities.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
