{"id":"https://openalex.org/W2021868939","doi":"https://doi.org/10.1109/ijcnn.2013.6706804","title":"Heteroscedastic Gaussian based correction term for fisher discriminant analysis and its kernel extension","display_name":"Heteroscedastic Gaussian based correction term for fisher discriminant analysis and its kernel extension","publication_year":2013,"publication_date":"2013-08-01","ids":{"openalex":"https://openalex.org/W2021868939","doi":"https://doi.org/10.1109/ijcnn.2013.6706804","mag":"2021868939"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2013.6706804","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6706804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 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/A5039764322","display_name":"Tatsuya Yokota","orcid":"https://orcid.org/0000-0002-7368-2060"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Yokota","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo","[Tokyo Institute of Technology, Tokyo, JAPAN]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo","institution_ids":["https://openalex.org/I114531698"]},{"raw_affiliation_string":"[Tokyo Institute of Technology, Tokyo, JAPAN]","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076148615","display_name":"Toru Wakahara","orcid":"https://orcid.org/0000-0002-2091-3363"},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toru Wakahara","raw_affiliation_strings":["Hosei University, Tokyo","Hosei University Tokyo. Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hosei University, Tokyo","institution_ids":["https://openalex.org/I204291657"]},{"raw_affiliation_string":"Hosei University Tokyo. Japan","institution_ids":["https://openalex.org/I204291657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091563396","display_name":"Yukihiko Yamashita","orcid":"https://orcid.org/0000-0002-9757-3117"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukihiko Yamashita","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo","[Tokyo Institute of Technology, Tokyo, JAPAN]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo","institution_ids":["https://openalex.org/I114531698"]},{"raw_affiliation_string":"[Tokyo Institute of Technology, Tokyo, JAPAN]","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09447526,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9952999949455261,"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":0.9952999949455261,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9670000076293945,"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/bhattacharyya-distance","display_name":"Bhattacharyya distance","score":0.9096853137016296},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.7823909521102905},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6532381772994995},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6432433128356934},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5850448608398438},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.5746917724609375},{"id":"https://openalex.org/keywords/homoscedasticity","display_name":"Homoscedasticity","score":0.5505247712135315},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5339708924293518},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5142704844474792},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4797893166542053},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.47610247135162354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45725104212760925},{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.4506811797618866},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41399717330932617},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3639541268348694},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3307875990867615},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3049430251121521},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.27320951223373413}],"concepts":[{"id":"https://openalex.org/C24145651","wikidata":"https://www.wikidata.org/wiki/Q2901249","display_name":"Bhattacharyya distance","level":2,"score":0.9096853137016296},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.7823909521102905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6532381772994995},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6432433128356934},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5850448608398438},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.5746917724609375},{"id":"https://openalex.org/C104409967","wikidata":"https://www.wikidata.org/wiki/Q1054836","display_name":"Homoscedasticity","level":3,"score":0.5505247712135315},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5339708924293518},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5142704844474792},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4797893166542053},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.47610247135162354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45725104212760925},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.4506811797618866},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41399717330932617},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3639541268348694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3307875990867615},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3049430251121521},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.27320951223373413},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2013.6706804","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6706804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W155372691","https://openalex.org/W1614659291","https://openalex.org/W1770825568","https://openalex.org/W1985078322","https://openalex.org/W2009086942","https://openalex.org/W2041657594","https://openalex.org/W2046368494","https://openalex.org/W2086144086","https://openalex.org/W2098490982","https://openalex.org/W2108869194","https://openalex.org/W2118536426","https://openalex.org/W2124083668","https://openalex.org/W2130418330","https://openalex.org/W2135346934","https://openalex.org/W2150796457","https://openalex.org/W2164552042","https://openalex.org/W2171849959","https://openalex.org/W3120740533","https://openalex.org/W6674591255"],"related_works":["https://openalex.org/W2051354704","https://openalex.org/W2582496451","https://openalex.org/W2065460266","https://openalex.org/W2380616114","https://openalex.org/W2413996692","https://openalex.org/W2732151112","https://openalex.org/W2388528498","https://openalex.org/W3128240669","https://openalex.org/W2556943240","https://openalex.org/W2166803271"],"abstract_inverted_index":{"Fisher":[0],"discriminant":[1],"analysis":[2,8],"(FDA)":[3],"is":[4,31,61,92,111,135,155],"a":[5,46,52,150,158],"very":[6],"famous":[7],"method":[9,154,182],"for":[10,20,49],"classification.":[11],"However,":[12],"FDA":[13,30,50],"does":[14],"give":[15],"an":[16],"optimal":[17,33],"projection":[18],"only":[19],"Gaussian":[21,39,106],"distributions":[22],"with":[23,104],"equal":[24],"covariance":[25],"matrices.":[26],"In":[27,41],"other":[28],"words,":[29],"not":[32],"in":[34,126,157],"the":[35,57,68,94,99,113,118,124,127,152,180,184],"case":[36],"of":[37,129,161,172,179],"heteroscedastic":[38,105],"distributions.":[40,107,148],"this":[42],"paper,":[43],"we":[44],"propose":[45],"novel":[47],"criterion":[48,72],"including":[51],"correction":[53,95,114],"term":[54,96,115],"based":[55,71,97,116],"on":[56,98,117],"Bhattacharyya":[58,100],"distance":[59,70,101,120],"which":[60],"closely":[62],"related":[63],"to":[64,143,146],"classification":[65,162],"rate.":[66],"Furthermore,":[67],"Chernoff":[69,119],"and":[73,169],"its":[74,80],"kernelized":[75],"version":[76],"are":[77,140],"proposed":[78,83,153,181],"as":[79],"extension.":[81],"These":[82],"criteria":[84],"have":[85],"three":[86],"strong":[87],"points.":[88],"The":[89,108,132],"first":[90],"one":[91,110],"that":[93,112,136],"can":[102,121],"deal":[103],"second":[109],"handle":[122],"easily":[123,141],"difference":[125],"number":[128],"class":[130],"samples.":[131],"third":[133],"point":[134],"their":[137],"kernel":[138],"extensions":[139],"implemented":[142],"be":[144],"applied":[145],"non-Gaussian":[147],"As":[149],"result,":[151],"applicable":[156],"wide":[159],"variety":[160],"problems.":[163],"Experimental":[164],"results":[165],"using":[166],"toy":[167],"simulations":[168],"nine":[170],"kinds":[171],"UCI":[173],"real-world":[174],"datasets":[175],"show":[176],"marked":[177],"advantages":[178],"over":[183],"conventional":[185],"FDA.":[186]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}