{"id":"https://openalex.org/W3174330185","doi":"https://doi.org/10.1109/tkde.2021.3090275","title":"Deep Metric Learning for K Nearest Neighbor Classication","display_name":"Deep Metric Learning for K Nearest Neighbor Classication","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3174330185","doi":"https://doi.org/10.1109/tkde.2021.3090275","mag":"3174330185"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2021.3090275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3090275","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","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/A5102971261","display_name":"Tingting Liao","orcid":"https://orcid.org/0000-0002-6846-3306"},"institutions":[{"id":"https://openalex.org/I14116566","display_name":"Wuhan Polytechnic University","ror":"https://ror.org/05w0e5j23","country_code":"CN","type":"education","lineage":["https://openalex.org/I14116566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Liao","raw_affiliation_strings":["Mathematics and Computer Science, Wuhan Polytechnic University, 74615 Wuhan, Hubei, China, (e-mail: liaotingting2020@ia.ac.cn)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematics and Computer Science, Wuhan Polytechnic University, 74615 Wuhan, Hubei, China, (e-mail: liaotingting2020@ia.ac.cn)","institution_ids":["https://openalex.org/I14116566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109299788","display_name":"Zhen Lei","orcid":"https://orcid.org/0000-0002-0791-189X"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Lei","raw_affiliation_strings":["CBSR & NLPR, CASIA, Beijing, Beijing, China, (e-mail: zlei@nlpr.ia.ac.cn)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CBSR & NLPR, CASIA, Beijing, Beijing, China, (e-mail: zlei@nlpr.ia.ac.cn)","institution_ids":["https://openalex.org/I4210112150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036801346","display_name":"Tianqing Zhu","orcid":"https://orcid.org/0000-0003-3411-7947"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tianqing Zhu","raw_affiliation_strings":["School of Software, University of Technology Sydney, Sydney, New South Wales, Australia, (e-mail: Tianqing.Zhu@uts.edu.au)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, University of Technology Sydney, Sydney, New South Wales, Australia, (e-mail: Tianqing.Zhu@uts.edu.au)","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038865738","display_name":"Shan Zeng","orcid":"https://orcid.org/0000-0003-1142-5613"},"institutions":[{"id":"https://openalex.org/I14116566","display_name":"Wuhan Polytechnic University","ror":"https://ror.org/05w0e5j23","country_code":"CN","type":"education","lineage":["https://openalex.org/I14116566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Zeng","raw_affiliation_strings":["Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, HUBEI SHENG, China, (e-mail: zengshan1981@whpu.edu.cn)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, HUBEI SHENG, China, (e-mail: zengshan1981@whpu.edu.cn)","institution_ids":["https://openalex.org/I14116566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072875517","display_name":"Yaqin Li","orcid":"https://orcid.org/0000-0002-7431-2619"},"institutions":[{"id":"https://openalex.org/I14116566","display_name":"Wuhan Polytechnic University","ror":"https://ror.org/05w0e5j23","country_code":"CN","type":"education","lineage":["https://openalex.org/I14116566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaqin Li","raw_affiliation_strings":["Mathematics and Computer Science, Wuhan Polytechnic University, 74615 Wuhan, Hubei, China, (e-mail: leeyaqin@whpu.edu.cn)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematics and Computer Science, Wuhan Polytechnic University, 74615 Wuhan, Hubei, China, (e-mail: leeyaqin@whpu.edu.cn)","institution_ids":["https://openalex.org/I14116566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074890965","display_name":"Yuan Cao","orcid":"https://orcid.org/0000-0002-1445-8210"},"institutions":[{"id":"https://openalex.org/I14116566","display_name":"Wuhan Polytechnic University","ror":"https://ror.org/05w0e5j23","country_code":"CN","type":"education","lineage":["https://openalex.org/I14116566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cao Yuan","raw_affiliation_strings":["Mathematics and Computer Science, Wuhan Polytechnic University, 74615 Wuhan, Hubei, China, (e-mail: yc@whpu.edu.cn)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematics and Computer Science, Wuhan Polytechnic University, 74615 Wuhan, Hubei, China, (e-mail: yc@whpu.edu.cn)","institution_ids":["https://openalex.org/I14116566"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3202,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.90098944,"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":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9980000257492065,"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/computer-science","display_name":"Computer science","score":0.8227255344390869},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7009057998657227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6864611506462097},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6232626438140869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6207051277160645},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6004116535186768},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5735377669334412},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5349498987197876},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.47824135422706604},{"id":"https://openalex.org/keywords/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.44212037324905396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4157343804836273},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41344478726387024},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33000966906547546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8227255344390869},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7009057998657227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6864611506462097},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6232626438140869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6207051277160645},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6004116535186768},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5735377669334412},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5349498987197876},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.47824135422706604},{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.44212037324905396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4157343804836273},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41344478726387024},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33000966906547546},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2021.3090275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2021.3090275","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7400000095367432,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W205159212","https://openalex.org/W1587362683","https://openalex.org/W1898424075","https://openalex.org/W1969600701","https://openalex.org/W2019762723","https://openalex.org/W2068514419","https://openalex.org/W2078238952","https://openalex.org/W2087295784","https://openalex.org/W2089611415","https://openalex.org/W2100495367","https://openalex.org/W2103250033","https://openalex.org/W2104752854","https://openalex.org/W2107686700","https://openalex.org/W2110798204","https://openalex.org/W2117154949","https://openalex.org/W2117553576","https://openalex.org/W2121647436","https://openalex.org/W2130060563","https://openalex.org/W2130556178","https://openalex.org/W2135624716","https://openalex.org/W2136137122","https://openalex.org/W2136261952","https://openalex.org/W2136922672","https://openalex.org/W2138451337","https://openalex.org/W2144935315","https://openalex.org/W2150800840","https://openalex.org/W2154579312","https://openalex.org/W2157364932","https://openalex.org/W2167999447","https://openalex.org/W2555897561","https://openalex.org/W2601450892","https://openalex.org/W2604763608","https://openalex.org/W2734377693","https://openalex.org/W2742093937","https://openalex.org/W2749338522","https://openalex.org/W2753160622","https://openalex.org/W2794203269","https://openalex.org/W2885201931","https://openalex.org/W2962723986","https://openalex.org/W2963283377","https://openalex.org/W2963341924","https://openalex.org/W2963775347","https://openalex.org/W2964080601","https://openalex.org/W2986604550","https://openalex.org/W3091905774","https://openalex.org/W3118608800","https://openalex.org/W3148981562","https://openalex.org/W6608394925","https://openalex.org/W6639352576","https://openalex.org/W6675655370","https://openalex.org/W6675751002","https://openalex.org/W6675955514","https://openalex.org/W6676481782","https://openalex.org/W6677328822","https://openalex.org/W6680332746","https://openalex.org/W6680962578","https://openalex.org/W6682751323","https://openalex.org/W6717697761","https://openalex.org/W6730323794","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6740756965","https://openalex.org/W6742288159","https://openalex.org/W6743661861","https://openalex.org/W6749873645","https://openalex.org/W6751281049","https://openalex.org/W6770404601","https://openalex.org/W6783596713","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2120164251","https://openalex.org/W2015098463","https://openalex.org/W2953235813","https://openalex.org/W1978452970","https://openalex.org/W2015442739","https://openalex.org/W2351157934","https://openalex.org/W2519241726","https://openalex.org/W2153677915","https://openalex.org/W4322721277","https://openalex.org/W128856181"],"abstract_inverted_index":{"KNN":[0],"has":[1,112,182],"gained":[2],"popularity":[3],"in":[4,57],"machine":[5],"learning":[6],"due":[7,48],"to":[8,32,49,64,76,87,98,127,152],"its":[9],"simplicity":[10],"and":[11,38,123,170,186],"good":[12],"performance.":[13],"However,":[14],"kNN":[15,124,134],"faces":[16],"two":[17,67],"problems":[18],"with":[19,116,132],"classification":[20,168],"tasks.":[21],"The":[22,41,119,179],"first":[23],"is":[24,30,43],"that":[25],"an":[26,143],"appropriate":[27],"distance":[28,91,154],"measurement":[29],"required":[31],"compute":[33],"distances":[34],"between":[35],"test":[36],"sample":[37],"training":[39,60,177],"samples.":[40],"other":[42],"the":[44,50,54,58,78,108,175],"highly":[45],"computational":[46],"complexity":[47],"requirement":[51],"of":[52,80,103,121,146,163,174],"searching":[53],"nearest":[55],"neighbors":[56],"whole":[59],"data.":[61],"In":[62],"order":[63],"mitigate":[65],"these":[66],"problems,":[68],"we":[69],"propose":[70],"a":[71,89,100,150,160,183,196],"novel":[72],"method":[73,181],"named":[74],"KCNN":[75,82,138,157],"enhance":[77],"performance":[79],"kNN.":[81],"uses":[83],"convolutional":[84],"neural":[85],"networks":[86],"learn":[88,99,153],"suitable":[90],"metric":[92],"as":[93,95,149],"well":[94],"prototype":[96],"reduction":[97],"reduced":[101,161],"set":[102,162],"prototypes":[104],"which":[105,133,165],"can":[106,135],"represent":[107],"original":[109],"set.":[110,178],"It":[111],"several":[113],"superiorities":[114],"compared":[115],"related":[117],"methods.":[118],"combination":[120],"CNN":[122],"empowers":[125],"it":[126,148],"extract":[128],"discriminative":[129],"hierarchical":[130],"features":[131],"easily":[136],"classify.":[137],"learns":[139,159],"spatial":[140],"information":[141],"on":[142],"image":[144],"instead":[145],"considering":[147],"vector":[151],"metric.":[155],"Moreover,":[156],"simultaneously":[158],"prototypes,":[164],"help":[166],"improve":[167],"efficiency":[169],"avoid":[171],"noisy":[172],"samples":[173],"massive":[176],"proposed":[180],"better":[184],"robustness":[185],"convergence":[187],"than":[188],"CNN,":[189],"especially":[190],"when":[191],"projecting":[192],"input":[193],"data":[194],"into":[195],"low-dimension":[197],"space.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
