{"id":"https://openalex.org/W2067772694","doi":"https://doi.org/10.1109/cvpr.2010.5539952","title":"Bayes optimal kernel discriminant analysis","display_name":"Bayes optimal kernel discriminant analysis","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2067772694","doi":"https://doi.org/10.1109/cvpr.2010.5539952","mag":"2067772694"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5539952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","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/A5076623908","display_name":"Di You","orcid":"https://orcid.org/0000-0003-2479-9323"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Di You","raw_affiliation_strings":["Ohio State Uinversity, Columbus, OH, USA","[Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA]"],"affiliations":[{"raw_affiliation_string":"Ohio State Uinversity, Columbus, OH, USA","institution_ids":["https://openalex.org/I52357470"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA]","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085208414","display_name":"Aleix M. Mart\u0131\u0301nez","orcid":"https://orcid.org/0000-0002-4745-4953"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aleix M. Martinez","raw_affiliation_strings":["Ohio State Uinversity, Columbus, OH, USA","[Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA]"],"affiliations":[{"raw_affiliation_string":"Ohio State Uinversity, Columbus, OH, USA","institution_ids":["https://openalex.org/I52357470"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA]","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076623908"],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":1.2931,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81873223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3533","last_page":"3538"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9976999759674072,"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":0.9976999759674072,"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/T10320","display_name":"Neural Networks and Applications","score":0.9909999966621399,"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/T12676","display_name":"Machine Learning and ELM","score":0.9889000058174133,"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-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.7903821468353271},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7681005001068115},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.6883206963539124},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.6567719578742981},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5612784624099731},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5567356944084167},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.5513161420822144},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5497735142707825},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5417438745498657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5234850645065308},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.5232535600662231},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.47185277938842773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3875180780887604},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.20137330889701843},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.18020567297935486}],"concepts":[{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.7903821468353271},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7681005001068115},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.6883206963539124},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.6567719578742981},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5612784624099731},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5567356944084167},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.5513161420822144},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5497735142707825},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5417438745498657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5234850645065308},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5232535600662231},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.47185277938842773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3875180780887604},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.20137330889701843},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.18020567297935486},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2010.5539952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.177.7847","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.177.7847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www2.ece.ohio-state.edu/%7Ealeix/cvpr10.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1560724230","https://openalex.org/W1591385104","https://openalex.org/W2021012145","https://openalex.org/W2041657594","https://openalex.org/W2046649434","https://openalex.org/W2052570068","https://openalex.org/W2068484625","https://openalex.org/W2084812512","https://openalex.org/W2085535170","https://openalex.org/W2106907649","https://openalex.org/W2111574755","https://openalex.org/W2112074816","https://openalex.org/W2129302305","https://openalex.org/W2130418330","https://openalex.org/W2137611320","https://openalex.org/W2140389641","https://openalex.org/W2142387771","https://openalex.org/W2146439239","https://openalex.org/W2146766088","https://openalex.org/W2150796457","https://openalex.org/W2152967416","https://openalex.org/W2163771264","https://openalex.org/W2164071167","https://openalex.org/W2164552042","https://openalex.org/W2982720039","https://openalex.org/W2994340921","https://openalex.org/W3195149063","https://openalex.org/W4238240379","https://openalex.org/W4246066915","https://openalex.org/W4298876635","https://openalex.org/W6635552349","https://openalex.org/W6680751671"],"related_works":["https://openalex.org/W2393746448","https://openalex.org/W1984421104","https://openalex.org/W2071590642","https://openalex.org/W2366185040","https://openalex.org/W2127229869","https://openalex.org/W2512565647","https://openalex.org/W2027376491","https://openalex.org/W2048352225","https://openalex.org/W2046363782","https://openalex.org/W2905418897"],"abstract_inverted_index":{"Kernel":[0],"methods":[1],"provide":[2],"an":[3],"efficient":[4,49],"mechanism":[5],"to":[6,61,74,121],"derive":[7],"nonlinear":[8,72],"algorithms.":[9],"In":[10],"classification":[11,105],"problems":[12,130],"as":[13,15],"well":[14],"in":[16,92,107,125],"feature":[17],"extraction,":[18],"kernel-based":[19],"approaches":[20],"map":[21],"the":[22,36,57,63,66,70,86,89,98,103,108,118,126,139,142,146,149],"originally":[23],"nonlinearly":[24],"separable":[25,40],"data":[26,37],"into":[27],"a":[28,53,75,81],"space":[29,128],"of":[30,65,88,141,148],"intrinsically":[31],"much":[32],"higher":[33],"dimensionality":[34],"where":[35],"is":[38,60],"linearly":[39],"and":[41,48],"can":[42,116],"be":[43],"readily":[44],"classified":[45],"with":[46,131],"existing":[47],"linear":[50,76],"methods.":[51],"For":[52],"given":[54],"kernel":[55,67,90,99,109,127],"function,":[56],"main":[58],"challenge":[59],"determine":[62],"parameters":[64,91,100],"which":[68],"maps":[69],"original":[71],"problem":[73],"one.":[77],"This":[78],"paper":[79],"derives":[80],"Bayes":[82],"optimal":[83],"criterion":[84,96,120,144],"for":[85,129],"selection":[87,124],"discriminant":[93],"analysis.":[94],"Our":[95],"selects":[97],"that":[101],"maximize":[102],"(Bayes)":[104],"accuracy":[106],"space.":[110],"We":[111],"also":[112],"show":[113],"how":[114],"we":[115],"use":[117],"same":[119],"do":[122],"subclass":[123],"multimodal":[132],"class":[133],"distributions.":[134],"Extensive":[135],"experimental":[136],"evaluation":[137],"demonstrates":[138],"superiority":[140],"proposed":[143],"over":[145],"state":[147],"art.":[150]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":4},{"year":2012,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}