{"id":"https://openalex.org/W2125369647","doi":"https://doi.org/10.1145/1273496.1273514","title":"Local similarity discriminant analysis","display_name":"Local similarity discriminant analysis","publication_year":2007,"publication_date":"2007-06-20","ids":{"openalex":"https://openalex.org/W2125369647","doi":"https://doi.org/10.1145/1273496.1273514","mag":"2125369647"},"language":"en","primary_location":{"id":"doi:10.1145/1273496.1273514","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1273496.1273514","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th international conference on Machine learning","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/A5026977722","display_name":"Luca Cazzanti","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Luca Cazzanti","raw_affiliation_strings":["University of Washington, Seattle, WA","University Of Washington (Seattle, WA)"],"affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University Of Washington (Seattle, WA)","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111921762","display_name":"Maya R. Gupta","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maya R. Gupta","raw_affiliation_strings":["University of Washington, Seattle, WA","University Of Washington (Seattle, WA)"],"affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University Of Washington (Seattle, WA)","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026977722"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":4.2197,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.94528134,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"137","last_page":"144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9993000030517578,"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":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9876000285148621,"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/T10320","display_name":"Neural Networks and Applications","score":0.9854999780654907,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7497707605361938},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7424361109733582},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.7193235158920288},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6728552579879761},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.6344110369682312},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6092676520347595},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.6054922342300415},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5519551038742065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5270852446556091},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.49375519156455994},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.47703999280929565},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.44992953538894653},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.44636863470077515},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4367872178554535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4246140122413635},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.41452500224113464},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33500468730926514}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7497707605361938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7424361109733582},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.7193235158920288},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6728552579879761},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.6344110369682312},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6092676520347595},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.6054922342300415},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5519551038742065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5270852446556091},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.49375519156455994},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.47703999280929565},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.44992953538894653},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.44636863470077515},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4367872178554535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4246140122413635},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.41452500224113464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33500468730926514},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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:10.1145/1273496.1273514","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1273496.1273514","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th international conference on Machine learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"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":37,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1509562192","https://openalex.org/W1564947197","https://openalex.org/W1592033656","https://openalex.org/W1979662950","https://openalex.org/W1987551799","https://openalex.org/W2003048487","https://openalex.org/W2004131797","https://openalex.org/W2010584937","https://openalex.org/W2015186727","https://openalex.org/W2018490621","https://openalex.org/W2057175746","https://openalex.org/W2059975159","https://openalex.org/W2064884852","https://openalex.org/W2074045464","https://openalex.org/W2077990749","https://openalex.org/W2084812512","https://openalex.org/W2095306443","https://openalex.org/W2099111195","https://openalex.org/W2105152330","https://openalex.org/W2118216287","https://openalex.org/W2119667862","https://openalex.org/W2131473842","https://openalex.org/W2135807716","https://openalex.org/W2137291015","https://openalex.org/W2150993751","https://openalex.org/W2157656721","https://openalex.org/W2165828254","https://openalex.org/W2166299063","https://openalex.org/W2967677465","https://openalex.org/W4285719527","https://openalex.org/W4299551239","https://openalex.org/W6635458896","https://openalex.org/W6665698582","https://openalex.org/W6677557080","https://openalex.org/W6679617618","https://openalex.org/W6823533423"],"related_works":["https://openalex.org/W2088711785","https://openalex.org/W2499112530","https://openalex.org/W2362413895","https://openalex.org/W4283655540","https://openalex.org/W4285246984","https://openalex.org/W2034569211","https://openalex.org/W2075660794","https://openalex.org/W2146026567","https://openalex.org/W2366124773","https://openalex.org/W2341643496"],"abstract_inverted_index":{"We":[0,64],"propose":[1],"a":[2,32,72,113],"local,":[3],"generative":[4,114],"model":[5],"for":[6],"similarity-based":[7],"classification.":[8],"The":[9,24,40],"method":[10],"is":[11,100],"applicable":[12],"to":[13,54,71,75,87],"the":[14,27,56,66,76,81,88,105,110],"case":[15],"that":[16,97],"only":[17],"pairwise":[18],"similarities":[19],"between":[20],"samples":[21],"are":[22,45],"available.":[23],"classifier":[25],"models":[26],"local":[28,57,69,77,98],"class-conditional":[29],"distribution":[30],"using":[31],"maximum":[33],"entropy":[34],"estimate":[35],"and":[36,51,86,104],"empirical":[37],"moment":[38],"constraints.":[39],"resulting":[41],"exponential":[42],"class":[43,48],"conditional-distributions":[44],"combined":[46],"with":[47,102],"prior":[49],"probabilities":[50],"misclassification":[52],"costs":[53],"form":[55],"similarity":[58],"discriminant":[59],"analysis":[60],"(local":[61],"SDA)":[62],"classifier.":[63,115],"compare":[65],"performance":[67],"of":[68,112],"SDA":[70,99],"non-local":[73],"version,":[74],"nearest":[78,82],"centroid":[79,83],"classifier,":[80,84],"k-NN,":[85],"recently-developed":[89],"potential":[90],"support":[91],"vector":[92],"machine":[93],"(PSVM).":[94],"Results":[95],"show":[96],"competitive":[101],"k-NN":[103],"computationally-demanding":[106],"PSVM":[107],"while":[108],"offering":[109],"advantages":[111]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"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":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
