{"id":"https://openalex.org/W2554755107","doi":"https://doi.org/10.1109/ijcnn.2016.7727740","title":"Kernel selection with evolutionary algorithm for multiple kernel independent component analysis","display_name":"Kernel selection with evolutionary algorithm for multiple kernel independent component analysis","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2554755107","doi":"https://doi.org/10.1109/ijcnn.2016.7727740","mag":"2554755107"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727740","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5024190612","display_name":"Peng Wu","orcid":"https://orcid.org/0000-0001-9049-9316"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]},{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Wu","raw_affiliation_strings":["Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China","Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]},{"raw_affiliation_string":"Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan, China","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081412949","display_name":"Qian Yin","orcid":"https://orcid.org/0000-0002-0354-5490"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Yin","raw_affiliation_strings":["Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020083340","display_name":"Ping Guo","orcid":"https://orcid.org/0000-0002-7122-1084"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Guo","raw_affiliation_strings":["Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2501,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55007248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"9","issue":null,"first_page":"4147","last_page":"4153"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9635000228881836,"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/kernel","display_name":"Kernel (algebra)","score":0.8142112493515015},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.6743801832199097},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.6081875562667847},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5771616697311401},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5700527429580688},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.5603952407836914},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.5547825694084167},{"id":"https://openalex.org/keywords/tree-kernel","display_name":"Tree kernel","score":0.5182254314422607},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4613523483276367},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.4224071502685547},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3944341838359833},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35825812816619873},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33576589822769165},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32982879877090454},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12717792391777039}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.8142112493515015},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.6743801832199097},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.6081875562667847},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5771616697311401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5700527429580688},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.5603952407836914},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5547825694084167},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.5182254314422607},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4613523483276367},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.4224071502685547},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3944341838359833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35825812816619873},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33576589822769165},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32982879877090454},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12717792391777039},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727740","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1578874166","https://openalex.org/W1588424744","https://openalex.org/W1964939967","https://openalex.org/W1975848319","https://openalex.org/W1976137296","https://openalex.org/W2008204335","https://openalex.org/W2033244070","https://openalex.org/W2052849307","https://openalex.org/W2105647435","https://openalex.org/W2109743529","https://openalex.org/W2145295623","https://openalex.org/W2152195021","https://openalex.org/W2154462399","https://openalex.org/W2160034376","https://openalex.org/W2161813894","https://openalex.org/W2208561700","https://openalex.org/W2373426724","https://openalex.org/W6634514285","https://openalex.org/W6641446668","https://openalex.org/W6675755857","https://openalex.org/W6683181193","https://openalex.org/W6684105480","https://openalex.org/W7029611983"],"related_works":["https://openalex.org/W3100948281","https://openalex.org/W3013206934","https://openalex.org/W4311138679","https://openalex.org/W3123056048","https://openalex.org/W4291669689","https://openalex.org/W2071590642","https://openalex.org/W1983263273","https://openalex.org/W2393746448","https://openalex.org/W1984421104","https://openalex.org/W2558026684"],"abstract_inverted_index":{"Kernel":[0],"independent":[1],"component":[2],"analysis":[3],"(KICA)":[4],"has":[5,69],"an":[6,78],"important":[7],"application":[8],"in":[9,13,52,91],"blind":[10],"source":[11],"separation,":[12],"which":[14],"how":[15],"to":[16,82,133],"select":[17,83],"the":[18,22,30,35,47,84,100,119,135,139,168],"optimal":[19,36],"kernel,":[20],"including":[21],"kernel":[23,42,48,60,85,109,121,163],"functional":[24],"form":[25],"and":[26,151],"its":[27],"parameters,":[28],"is":[29,43,62,89,104,124,131],"key":[31],"issue":[32],"for":[33],"obtaining":[34],"performance.":[37],"In":[38],"practices,":[39],"a":[40,58,63,107,111],"single":[41,116,120],"usually":[44],"chosen":[45],"as":[46],"model":[49,61,86,160],"of":[50,54,87,99,114,138,170],"KICA":[51,88],"light":[53],"experience.":[55,72],"However,":[56],"selecting":[57],"suitable":[59],"more":[64],"difficult":[65],"problem":[66],"if":[67],"one":[68,103],"not":[70],"sufficient":[71],"To":[73],"deal":[74],"with":[75,161],"this":[76,92],"problem,":[77],"evolution":[79],"based":[80],"method":[81],"proposed":[90,101],"paper.":[93],"There":[94],"are":[95],"two":[96],"main":[97],"features":[98],"method:":[102],"that":[105,125,156],"using":[106,157],"multiple":[108,158],"model,":[110],"convex":[112],"combination":[113,136],"several":[115],"kernels,":[117],"replaces":[118],"model;":[122],"another":[123],"particle":[126],"swarm":[127],"optimization":[128],"(PSO)":[129],"algorithm":[130,165],"utilized":[132],"find":[134],"weights":[137],"composite":[140],"kernel.":[141],"Experiments":[142],"conducted":[143],"on":[144],"separating":[145],"one-dimensional":[146],"mixed":[147],"signals,":[148],"nature":[149],"images,":[150],"spectroscopic":[152],"CCD":[153],"images":[154],"showed":[155],"kernels":[159],"PSO":[162],"selection":[164],"can":[166],"enhance":[167],"performance":[169],"KICA.":[171]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
