{"id":"https://openalex.org/W2123252311","doi":"https://doi.org/10.1198/004017007000000074","title":"Graphical Tools for Quadratic Discriminant Analysis","display_name":"Graphical Tools for Quadratic Discriminant Analysis","publication_year":2007,"publication_date":"2007-04-19","ids":{"openalex":"https://openalex.org/W2123252311","doi":"https://doi.org/10.1198/004017007000000074","mag":"2123252311"},"language":"en","primary_location":{"id":"doi:10.1198/004017007000000074","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017007000000074","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-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/A5027353180","display_name":"Iain Pardoe","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133369","display_name":"Decision Sciences (United States)","ror":"https://ror.org/03gcvf773","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133369"]},{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Iain Pardoe","raw_affiliation_strings":["Department of Decision Sciences, Lundquist College of Business, University of Oregon Eugene, OR 97403","University of Oregon"],"affiliations":[{"raw_affiliation_string":"Department of Decision Sciences, Lundquist College of Business, University of Oregon Eugene, OR 97403","institution_ids":["https://openalex.org/I181233156","https://openalex.org/I4210133369"]},{"raw_affiliation_string":"University of Oregon","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059589977","display_name":"Xiangrong Yin","orcid":"https://orcid.org/0000-0001-9102-8647"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiangrong Yin","raw_affiliation_strings":["Department of Statistics, University of Georgia, Athens, GA 30605","University of Georgia,"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Georgia, Athens, GA 30605","institution_ids":["https://openalex.org/I165733156"]},{"raw_affiliation_string":"University of Georgia,","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042750705","display_name":"R. Dennis Cook","orcid":"https://orcid.org/0000-0002-3488-4743"},"institutions":[{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]},{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Dennis Cook","raw_affiliation_strings":["School of Statistics, University of Minnesota, Minneapolis, MN 55455","Statistics (Twin Cities)"],"affiliations":[{"raw_affiliation_string":"School of Statistics, University of Minnesota, Minneapolis, MN 55455","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"Statistics (Twin Cities)","institution_ids":["https://openalex.org/I4210101327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027353180"],"corresponding_institution_ids":["https://openalex.org/I181233156","https://openalex.org/I4210133369"],"apc_list":null,"apc_paid":null,"fwci":1.6439,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.85481998,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"49","issue":"2","first_page":"172","last_page":"183"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9878000020980835,"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":0.9878000020980835,"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":0.9866999983787537,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.973800003528595,"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/quadratic-classifier","display_name":"Quadratic classifier","score":0.8767637014389038},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.8410857915878296},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7170994877815247},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6049182415008545},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.6022952795028687},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.5806550979614258},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.5176078081130981},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5077889561653137},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5058445930480957},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.5038301348686218},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.43684983253479004},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.431466281414032},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4172993004322052},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3264746069908142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2744121253490448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.23567965626716614},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2002524733543396},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.06307476758956909}],"concepts":[{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.8767637014389038},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.8410857915878296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7170994877815247},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6049182415008545},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.6022952795028687},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5806550979614258},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.5176078081130981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5077889561653137},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5058445930480957},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.5038301348686218},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.43684983253479004},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.431466281414032},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4172993004322052},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3264746069908142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2744121253490448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.23567965626716614},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2002524733543396},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.06307476758956909},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1198/004017007000000074","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017007000000074","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.575.6781","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.575.6781","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.iainpardoe.com/research/07tech/cansave.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7599999904632568}],"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":46,"referenced_works":["https://openalex.org/W94571269","https://openalex.org/W98890896","https://openalex.org/W1513618424","https://openalex.org/W1599997964","https://openalex.org/W1603452536","https://openalex.org/W1703658160","https://openalex.org/W1770825568","https://openalex.org/W1913335695","https://openalex.org/W1973682096","https://openalex.org/W1992809818","https://openalex.org/W2001619934","https://openalex.org/W2012801463","https://openalex.org/W2013159914","https://openalex.org/W2025341678","https://openalex.org/W2032797100","https://openalex.org/W2038241909","https://openalex.org/W2040224646","https://openalex.org/W2050605581","https://openalex.org/W2053225089","https://openalex.org/W2062832364","https://openalex.org/W2065897728","https://openalex.org/W2068752849","https://openalex.org/W2069165648","https://openalex.org/W2079119297","https://openalex.org/W2091794125","https://openalex.org/W2091982937","https://openalex.org/W2099427332","https://openalex.org/W2116449426","https://openalex.org/W2129476886","https://openalex.org/W2135346934","https://openalex.org/W2149253979","https://openalex.org/W2156678280","https://openalex.org/W2163490846","https://openalex.org/W2186807866","https://openalex.org/W2289748525","https://openalex.org/W3102221121","https://openalex.org/W3112073073","https://openalex.org/W3133917342","https://openalex.org/W4229567225","https://openalex.org/W4231534395","https://openalex.org/W4241872460","https://openalex.org/W4242224377","https://openalex.org/W4247545505","https://openalex.org/W4250523173","https://openalex.org/W4253762510","https://openalex.org/W4254923048"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W4285246984","https://openalex.org/W2366124773","https://openalex.org/W1984080040","https://openalex.org/W2146026567","https://openalex.org/W1996980404","https://openalex.org/W2075660794","https://openalex.org/W2056011487","https://openalex.org/W2043029256","https://openalex.org/W2143240582"],"abstract_inverted_index":{"Sufficient":[0],"dimension-reduction":[1,19],"methods":[2],"provide":[3,93],"effective":[4],"ways":[5],"to":[6,32,45,83,97,113,136],"visualize":[7,98,108],"discriminant":[8,35,56,100],"analysis":[9,36,101],"problems.":[10],"For":[11],"example,":[12],"Cook":[13],"and":[14,131],"Yin":[15],"showed":[16],"that":[17,29],"the":[18,47,64,73,86,118],"method":[20],"of":[21,49,120],"sliced":[22],"average":[23],"variance":[24],"estimation":[25],"(SAVE)":[26],"identifies":[27],"variates":[28,51,66,92,106],"are":[30],"equivalent":[31],"a":[33,68,94],"quadratic":[34],"(QDA)":[37],"solution.":[38],"This":[39,110],"article":[40],"makes":[41],"this":[42],"connection":[43],"explicit":[44],"motivate":[46],"use":[48],"SAVE":[50,65,105,138],"in":[52,125],"exploratory":[53],"graphics":[54],"for":[55,123],"analysis.":[57],"Classification":[58],"can":[59,133],"then":[60,79],"be":[61,114,134],"based":[62],"on":[63],"using":[67,85],"suitable":[69],"distance":[70],"measure.":[71],"If":[72],"chosen":[74],"measure":[75],"is":[76,81,139],"Mahalanobis":[77],"distance,":[78],"classification":[80],"identical":[82],"QDA":[84,124,132],"original":[87],"variables.":[88],"Just":[89],"as":[90],"canonical":[91],"useful":[95,116],"way":[96],"linear":[99],"(LDA),":[102],"so":[103],"do":[104],"help":[107],"QDA.":[109],"would":[111],"appear":[112],"particularly":[115],"given":[117],"lack":[119],"graphical":[121],"tools":[122],"current":[126],"software.":[127],"Furthermore,":[128],"whereas":[129],"LDA":[130],"sensitive":[135],"nonnormality,":[137],"more":[140],"robust.":[141]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":6}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
