{"id":"https://openalex.org/W2541462808","doi":"https://doi.org/10.1587/transinf.2016edp7081","title":"Optimum Nonlinear Discriminant Analysis and Discriminant Kernel Support Vector Machine","display_name":"Optimum Nonlinear Discriminant Analysis and Discriminant Kernel Support Vector Machine","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2541462808","doi":"https://doi.org/10.1587/transinf.2016edp7081","mag":"2541462808"},"language":"en","primary_location":{"id":"doi:10.1587/transinf.2016edp7081","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2016edp7081","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E99.D/11/E99.D_2016EDP7081/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/transinf/E99.D/11/E99.D_2016EDP7081/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108778446","display_name":"Akinori Hidaka","orcid":null},"institutions":[{"id":"https://openalex.org/I165522056","display_name":"Tokyo Denki University","ror":"https://ror.org/01pa62v70","country_code":"JP","type":"education","lineage":["https://openalex.org/I165522056"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akinori HIDAKA","raw_affiliation_strings":["Tokyo Denki University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Denki University","institution_ids":["https://openalex.org/I165522056"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086456461","display_name":"Takio Kurita","orcid":"https://orcid.org/0000-0003-3982-6750"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takio KURITA","raw_affiliation_strings":["Hiroshima University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University","institution_ids":["https://openalex.org/I113306721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1657,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5772187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"E99.D","issue":"11","first_page":"2734","last_page":"2744"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987000226974487,"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":0.9987000226974487,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.989300012588501,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.879437267780304},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.8376070261001587},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.7430343627929688},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6690990328788757},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.6587243676185608},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6441184282302856},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.6135038733482361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5900543332099915},{"id":"https://openalex.org/keywords/multiple-discriminant-analysis","display_name":"Multiple discriminant analysis","score":0.5522570013999939},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5467821359634399},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4822703003883362},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4721873998641968},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45038604736328125},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4398342967033386},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3285607695579529},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05858531594276428}],"concepts":[{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.879437267780304},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.8376070261001587},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.7430343627929688},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6690990328788757},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6587243676185608},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6441184282302856},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.6135038733482361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5900543332099915},{"id":"https://openalex.org/C58596280","wikidata":"https://www.wikidata.org/wiki/Q28324849","display_name":"Multiple discriminant analysis","level":3,"score":0.5522570013999939},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5467821359634399},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4822703003883362},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4721873998641968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45038604736328125},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4398342967033386},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3285607695579529},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05858531594276428},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transinf.2016edp7081","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2016edp7081","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E99.D/11/E99.D_2016EDP7081/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1587/transinf.2016edp7081","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2016edp7081","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E99.D/11/E99.D_2016EDP7081/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.800000011920929}],"awards":[{"id":"https://openalex.org/G5596175846","display_name":"A study on classifier design by integration of the probabilistic evidences and it applications to image recognitions","funder_award_id":"23500211","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G961838086","display_name":null,"funder_award_id":"16H01430","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2541462808.pdf","grobid_xml":"https://content.openalex.org/works/W2541462808.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W98857008","https://openalex.org/W161485605","https://openalex.org/W1510526001","https://openalex.org/W2001619934","https://openalex.org/W2027717478","https://openalex.org/W2041657594","https://openalex.org/W2050436009","https://openalex.org/W2067772694","https://openalex.org/W2069683962","https://openalex.org/W2104978738","https://openalex.org/W2106202111","https://openalex.org/W2108146080","https://openalex.org/W2115969778","https://openalex.org/W2132005946","https://openalex.org/W2140389641","https://openalex.org/W2147947791","https://openalex.org/W2150796457","https://openalex.org/W2163771264","https://openalex.org/W2166473218","https://openalex.org/W2166834727","https://openalex.org/W3120421331"],"related_works":["https://openalex.org/W2141981133","https://openalex.org/W2166834727","https://openalex.org/W2501117152","https://openalex.org/W2108146080","https://openalex.org/W2163911222","https://openalex.org/W1542789055","https://openalex.org/W2109101449","https://openalex.org/W2354551251","https://openalex.org/W1963649114","https://openalex.org/W2025089370"],"abstract_inverted_index":{"Kernel":[0],"discriminant":[1,10,25,37,54,102,145],"analysis":[2,11,55],"(KDA)":[3],"is":[4,39,42,46,62,106],"the":[5,16,35,49,59,67,82,111,138],"mainstream":[6],"approach":[7,33],"of":[8,51,57,137],"nonlinear":[9,24,36,53,60],"(NDA).":[12],"Since":[13],"it":[14],"uses":[15],"kernel":[17,99,103],"trick,":[18],"KDA":[19],"does":[20],"not":[21],"consider":[22],"its":[23],"mapping":[26,38,61],"explicitly.":[27],"In":[28,114],"this":[29,115],"paper,":[30,116],"another":[31],"NDA":[32,76,118],"where":[34],"analytically":[40],"given":[41],"developed.":[43],"This":[44,71],"study":[45],"based":[47],"on":[48],"theory":[50,72],"optimal":[52],"(ONDA)":[56],"which":[58,105],"exactly":[63],"expressed":[64],"by":[65,80,108],"using":[66,110],"Bayesian":[68,83,139],"posterior":[69,84,112,126,140],"probability.":[70],"indicates":[73],"that":[74],"various":[75,89],"can":[77],"be":[78],"derived":[79,121],"estimating":[81],"probability":[85,127],"in":[86],"ONDA":[87,93,123],"with":[88,124],"estimation":[90,135],"methods.":[91],"Also,":[92],"brings":[94],"an":[95],"insight":[96],"about":[97],"novel":[98],"functions,":[100],"called":[101],"(DK),":[104],"defined":[107],"also":[109],"probabilities.":[113],"several":[117,125],"and":[119,131],"DK":[120],"from":[122],"estimators":[128],"are":[129],"developed":[130],"evaluated.":[132],"Given":[133],"fine":[134],"methods":[136],"probability,":[141],"they":[142],"give":[143],"good":[144],"spaces":[146],"for":[147],"visualization":[148],"or":[149],"classification.":[150]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2025-10-10T00:00:00"}
