{"id":"https://openalex.org/W4300868237","doi":"https://doi.org/10.21437/icslp.2000-885","title":"Weighted pairwise scatter to improve linear discriminant analysis","display_name":"Weighted pairwise scatter to improve linear discriminant analysis","publication_year":2000,"publication_date":"2000-10-16","ids":{"openalex":"https://openalex.org/W4300868237","doi":"https://doi.org/10.21437/icslp.2000-885"},"language":"en","primary_location":{"id":"doi:10.21437/icslp.2000-885","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.2000-885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"6th International Conference on Spoken Language Processing (ICSLP 2000)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100405257","display_name":"Yongxin Li","orcid":"https://orcid.org/0009-0001-2870-6975"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongxin Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074146436","display_name":"Yuqing Gao","orcid":"https://orcid.org/0000-0001-8709-8378"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuqing Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065994318","display_name":"Hakan Erdo\u011fan","orcid":"https://orcid.org/0000-0003-3140-8642"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hakan Erdogan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"vol. 4, 608","last_page":"611"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.6039999723434448,"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":0.6039999723434448,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.8325551748275757},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.8209919929504395},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6197347044944763},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.5750131607055664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.546431839466095},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.46134689450263977},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.444654256105423},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3202856481075287}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.8325551748275757},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.8209919929504395},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6197347044944763},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.5750131607055664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.546431839466095},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.46134689450263977},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.444654256105423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3202856481075287}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/icslp.2000-885","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.2000-885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"6th International Conference on Spoken Language Processing (ICSLP 2000)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.124.5651","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://people.sabanciuniv.edu/~haerdogan/pubs/li00wps.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1999647744","https://openalex.org/W2350751952","https://openalex.org/W2362114017","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W2375208160","https://openalex.org/W2113920489","https://openalex.org/W2371177901","https://openalex.org/W1534459252","https://openalex.org/W1984472287"],"abstract_inverted_index":{"Linear":[0],"Discriminant":[1],"Analysis":[2],"(LDA)":[3],"aims":[4],"to":[5,11,43,108,123],"transform":[6],"an":[7],"original":[8],"feature":[9],"space":[10,15],"a":[12,26,51,79,99,143,170],"lower":[13],"dimensional":[14],"with":[16,155],"as":[17,22],"little":[18],"loss":[19],"in":[20,46,78,93],"discrimination":[21,45],"possible.":[23],"We":[24,97,128],"introduce":[25,98],"novel":[27],"LDA":[28],"matrix":[29,55],"computation":[30],"that":[31,56,85,106,133],"incorporates":[32],"confusability":[33,111],"information":[34,112],"between":[35,52,74,115,140],"classes":[36,141],"into":[37,113],"the":[38,60,66,73,94,110,114,125,161],"transform.":[39],"Our":[40],"goal":[41],"is":[42,57,69,89,147],"improve":[44],"LDA.":[47,96],"In":[48],"conventional":[49,95],"LDA,":[50],"class":[53,63,75,87,116],"covariance":[54,76,117],"based":[58,158],"on":[59,135,169],"scatter":[61,105],"of":[62,131],"means":[64],"around":[65],"global":[67],"mean":[68],"used.":[70],"By":[71],"rewriting":[72],"expression":[77],"more":[80],"revealing":[81],"way,":[82],"we":[83],"unveil":[84],"each":[86,103,150],"pair":[88],"considered":[90],"equally":[91],"confusable":[92],"weighting":[100,126],"factor":[101],"for":[102,149],"pairwise":[104],"enables":[107],"integrate":[109],"matrix.":[118],"There":[119],"are":[120],"many":[121],"possibilities":[122],"choose":[124],"factors.":[127],"consider":[129],"few":[130],"them":[132],"depend":[134],"Euclidean":[136],"and":[137],"Kullback-Leibler":[138],"distances":[139],"when":[142],"single":[144],"Gaussian":[145],"approximation":[146],"used":[148],"class.":[151],"The":[152],"method":[153],"combined":[154],"speaker":[156],"cluster":[157],"transformation":[159],"decreases":[160],"error":[162],"rate":[163],"by":[164],"about":[165],"relative":[166],"10":[167],"%":[168],"large":[171],"vocabulary":[172],"speech":[173,178],"recognition":[174,179],"task":[175],"using":[176],"IBM\u2019s":[177],"engine.":[180],"1.":[181]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
