{"id":"https://openalex.org/W1988344179","doi":"https://doi.org/10.1145/1401890.1401918","title":"Structured metric learning for high dimensional problems","display_name":"Structured metric learning for high dimensional problems","publication_year":2008,"publication_date":"2008-08-24","ids":{"openalex":"https://openalex.org/W1988344179","doi":"https://doi.org/10.1145/1401890.1401918","mag":"1988344179"},"language":"en","primary_location":{"id":"doi:10.1145/1401890.1401918","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5088203996","display_name":"Jason V. Davis","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jason V. Davis","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA","University of Texas at Austin, Austin, TX USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"University of Texas at Austin, Austin, TX USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Inderjit S. Dhillon","raw_affiliation_strings":["University of Texas at Austin, Austin, TX, USA","University of Texas at Austin, Austin, TX USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"University of Texas at Austin, Austin, TX USA","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088203996"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":7.7259,"has_fulltext":false,"cited_by_count":76,"citation_normalized_percentile":{"value":0.97569414,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"195","last_page":"203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9975000023841858,"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":0.9975000023841858,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.993399977684021,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9923999905586243,"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/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.7499995231628418},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7018194794654846},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.659771740436554},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5305557250976562},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5242317318916321},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5009539127349854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4777331054210663},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4683358669281006},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4559008777141571},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.446539968252182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4449540674686432},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4262450933456421},{"id":"https://openalex.org/keywords/cover-tree","display_name":"Cover tree","score":0.42449912428855896},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.41359788179397583},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.11075994372367859},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.10024666786193848}],"concepts":[{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.7499995231628418},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7018194794654846},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.659771740436554},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5305557250976562},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5242317318916321},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5009539127349854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4777331054210663},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4683358669281006},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4559008777141571},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.446539968252182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4449540674686432},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4262450933456421},{"id":"https://openalex.org/C53661774","wikidata":"https://www.wikidata.org/wiki/Q13108095","display_name":"Cover tree","level":5,"score":0.42449912428855896},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.41359788179397583},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.11075994372367859},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.10024666786193848},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1401890.1401918","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1660390307","https://openalex.org/W2104752854","https://openalex.org/W2106053110","https://openalex.org/W2117154949","https://openalex.org/W2130556178","https://openalex.org/W2147152072","https://openalex.org/W2151965112","https://openalex.org/W2159788726","https://openalex.org/W2169495281","https://openalex.org/W2296319761","https://openalex.org/W2798909945","https://openalex.org/W2799061466","https://openalex.org/W4205687621","https://openalex.org/W6637101025","https://openalex.org/W6675655370","https://openalex.org/W6750968397","https://openalex.org/W7014191107"],"related_works":["https://openalex.org/W2571415506","https://openalex.org/W1558159560","https://openalex.org/W4297079196","https://openalex.org/W2154600536","https://openalex.org/W4306317682","https://openalex.org/W23415291","https://openalex.org/W1985839407","https://openalex.org/W2291103301","https://openalex.org/W2390847229","https://openalex.org/W2070705506"],"abstract_inverted_index":{"The":[0,32],"success":[1],"of":[2,25,71,112,138,151,173,198],"popular":[3],"algorithms":[4,55,100],"such":[5,61,237],"as":[6,238],"k-means":[7],"clustering":[8],"or":[9,50,175],"nearest":[10,202],"neighbor":[11,203],"searches":[12],"depend":[13],"on":[14,129],"the":[15,18,28,113,130,196,215],"assumption":[16],"that":[17,46,83,101,164,180,207],"underlying":[19],"distance":[20,33,41,62],"functions":[21,63],"reflect":[22],"domain-specific":[23],"notions":[24],"similarity":[26],"for":[27,201],"problem":[29,36],"at":[30],"hand.":[31],"metric":[34,98,183,199],"learning":[35,99,200],"seeks":[37],"to":[38,44,59,77,79,168,190,230],"optimize":[39],"a":[40,126,149],"function":[42],"subject":[43],"constraints":[45],"arise":[47],"from":[48],"fully-supervised":[49],"semisupervised":[51],"information.":[52],"Several":[53],"recent":[54],"have":[56],"been":[57],"proposed":[58],"learn":[60],"in":[64,88,195],"low":[65],"dimensional":[66,81,153],"settings.":[67],"One":[68],"major":[69],"shortcoming":[70],"these":[72],"methods":[73,147,166,209,220,236],"is":[74,117],"their":[75],"failure":[76],"scale":[78,102,167],"high":[80,152],"problems":[82],"are":[84],"becoming":[85],"increasingly":[86],"ubiquitous":[87],"modern":[89],"data":[90,169],"mining":[91],"applications.":[92],"In":[93],"this":[94],"paper,":[95],"we":[96,144,205],"present":[97],"linearly":[103],"with":[104,171,188],"dimensionality,":[105],"permitting":[106],"efficient":[107,136],"optimization,":[108],"storage,":[109],"and":[110,160],"evaluation":[111],"learned":[114,182],"metric.":[115],"This":[116],"achieved":[118],"through":[119],"our":[120,146,165,181,208,219],"main":[121],"technical":[122],"contribution":[123],"which":[124,134],"provides":[125],"framework":[127],"based":[128],"log-determinant":[131],"matrix":[132],"divergence":[133],"enables":[135],"optimization":[137],"structured,":[139],"low-parameter":[140],"Mahalanobis":[141],"distances.":[142],"Experimentally,":[143],"evaluate":[145],"across":[148],"variety":[150],"domains,":[154],"including":[155],"text,":[156],"statistical":[157],"software":[158],"analysis,":[159],"collaborative":[161],"filtering,":[162],"showing":[163],"sets":[170],"tens":[172],"thousands":[174],"more":[176],"features.":[177],"We":[178],"show":[179,206],"can":[184],"achieve":[185,210],"excellent":[186],"quality":[187],"respect":[189],"various":[191],"criteria.":[192],"For":[193],"example,":[194],"context":[197],"classification,":[204],"24%":[211],"higher":[212,232],"accuracy":[213],"over":[214],"baseline":[216,235],"distance.":[217],"Additionally,":[218],"yield":[221],"very":[222],"good":[223],"precision":[224],"while":[225],"providing":[226],"recall":[227],"measures":[228],"up":[229],"20%":[231],"than":[233],"other":[234],"latent":[239],"semantic":[240],"analysis.":[241]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}