[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_291":292},"routes/_layout",{"_11":211},"routes/entity.$type.$id",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28},"dehydratedState",{"_74":75,"_76":77},"type","paper","id","paper_W2574388714","section","technologies","entity",{"_15":16,"_29":30,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":38,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":-5,"_49":50,"_51":30,"_52":53,"_54":55,"_56":57,"_58":59,"_60":61,"_62":-5,"_63":64,"_65":66,"_67":68,"_69":30,"_70":-5},"canonical","https://www.hugmap.com/entity/paper/paper_W2574388714/technologies","siteBaseUrl","https://www.hugmap.com","sectionCount",2,"jsonLd","{\"@context\":\"https://schema.org\",\"@graph\":[{\"name\":\"Learning k for kNN Classification\",\"url\":\"https://www.hugmap.com/entity/paper/paper_W2574388714/technologies\",\"@type\":\"ScholarlyArticle\",\"headline\":\"Learning k for kNN Classification\",\"datePublished\":\"2017-01-12\",\"isPartOf\":{\"@type\":\"Periodical\",\"name\":\"ACM Transactions on Intelligent Systems and Technology\"}},{\"@type\":\"CollectionPage\",\"name\":\"Learning k for kNN Classification 相关技术\",\"description\":\"Learning k for kNN Classification 在 HugMap 中的相关技术，共 2 条，含结构化关联信息与详情链接。\",\"url\":\"https://www.hugmap.com/entity/paper/paper_W2574388714/technologies\",\"inLanguage\":\"zh-CN\"},{\"@type\":\"BreadcrumbList\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"首页\",\"item\":\"https://www.hugmap.com/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"论文\",\"item\":\"https://www.hugmap.com/search?type=paper\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Learning k for kNN Classification\",\"item\":\"https://www.hugmap.com/entity/paper/paper_W2574388714\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"相关技术\",\"item\":\"https://www.hugmap.com/entity/paper/paper_W2574388714/technologies\"}]}]}","name","Learning k for kNN Classification","nameZh","aliases","description","descriptionZh","status","active","externalIds","{\"openalex\":\"W2574388714\",\"doi\":\"10.1145/2990508\"}","properties","source","openalex","createdAt","2026-05-15T18:16:40.205873208","updatedAt","2026-05-21T13:22:12.194546603","doi","10.1145/2990508","arxivId","openalexId","W2574388714","title","abstractText","The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an ℓ 1 -norm regularizer and an ℓ 2, 1 -norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.","publishedDate","2017-01-12","publishedYear",2017,"venue","ACM Transactions on Intelligent Systems and Technology","citationCount",558,"authors","keywords",[71,72,73],"pdfUrl","http://dl.acm.org/ft_gateway.cfm?id=2990508&type=pdf","topVenue",false,"displayName","displayDescription","Face and Expression Recognition","Sparse and Compressive Sensing Techniques","Industrial Vision Systems and Defect Detection","mutations",[],"queries",[78],{"_79":80,"_81":82,"_83":84,"_85":86},"dehydratedAt",1781173736729,"state",{"_88":89,"_90":91,"_92":80,"_93":-5,"_94":95,"_96":95,"_97":95,"_98":-5,"_99":-5,"_100":68,"_35":101,"_102":103},"queryKey",[87,19,14,16],"queryHash","[\"portal\",\"entity\",\"paper\",\"paper_W2574388714\"]","portal","data",{"_19":20,"_104":105,"_106":107,"_108":109,"_110":111,"_18":112,"_62":113},"dataUpdateCount",1,"dataUpdatedAt","error","errorUpdateCount",0,"errorUpdatedAt","fetchFailureCount","fetchFailureReason","fetchMeta","isInvalidated","success","fetchStatus","idle","entityType","Paper","taxonomies",[178,179,180],"events",[],"articles",[],[158,159],[114,115,116,117,118],{"_15":152,"_29":153,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":154,"_39":-5,"_40":41,"_42":155,"_44":156,"_124":157,"_51":-5,"_126":-5,"_127":-5,"_128":-5,"_60":-5,"_129":-5,"_130":-5,"_131":-5,"_132":-5,"_133":-5,"_69":153,"_70":-5},{"_15":146,"_29":147,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":148,"_39":-5,"_40":41,"_42":149,"_44":150,"_124":151,"_51":-5,"_126":-5,"_127":-5,"_128":-5,"_60":-5,"_129":-5,"_130":-5,"_131":-5,"_132":-5,"_133":-5,"_69":147,"_70":-5},{"_15":140,"_29":141,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":142,"_39":-5,"_40":41,"_42":143,"_44":144,"_124":145,"_51":-5,"_126":-5,"_127":-5,"_128":-5,"_60":-5,"_129":-5,"_130":-5,"_131":-5,"_132":-5,"_133":-5,"_69":141,"_70":-5},{"_15":134,"_29":135,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":136,"_39":-5,"_40":41,"_42":137,"_44":138,"_124":139,"_51":-5,"_126":-5,"_127":-5,"_128":-5,"_60":-5,"_129":-5,"_130":-5,"_131":-5,"_132":-5,"_133":-5,"_69":135,"_70":-5},{"_15":119,"_29":120,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":121,"_39":-5,"_40":41,"_42":122,"_44":123,"_124":125,"_51":-5,"_126":-5,"_127":-5,"_128":-5,"_60":-5,"_129":-5,"_130":-5,"_131":-5,"_132":-5,"_133":-5,"_69":120,"_70":-5},"person_A5100740143","Xuelong Li","{\"openalex\":\"A5100740143\",\"orcid\":\"0000-0002-0019-4197\"}","2026-05-14T03:29:11.310085066","2026-05-22T13:13:12.206930425","orcid","0000-0002-0019-4197","affiliation","researchArea","paperCount","homepage","linkedin","twitter","github","hindex","person_A5101835777","Ming Zong","{\"openalex\":\"A5101835777\",\"orcid\":\"0000-0001-5425-6667\"}","2026-05-14T08:11:24.398896036","2026-05-21T22:39:41.216761131","0000-0001-5425-6667","person_A5037340898","Xiaofeng Zhu","{\"openalex\":\"A5037340898\",\"orcid\":\"0000-0001-6840-0578\"}","2026-05-14T08:11:25.315953262","2026-05-23T10:36:03.474920738","0000-0001-6840-0578","person_A5065281913","Debo Cheng","{\"openalex\":\"A5065281913\",\"orcid\":\"0000-0002-0383-1462\"}","2026-05-15T11:29:59.183768257","2026-05-21T22:39:41.19887202","0000-0002-0383-1462","person_A5100764176","Shichao Zhang","{\"openalex\":\"A5100764176\",\"orcid\":\"0000-0001-6558-7875\"}","2026-05-16T09:15:13.196702304","2026-05-19T04:33:51.207533728","0000-0001-6558-7875",{"_15":174,"_29":73,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":175,"_39":-5,"_40":162,"_42":176,"_44":177,"_165":166,"_167":168,"_169":-5,"_170":171,"_172":-5,"_128":-5,"_173":-5,"_69":73,"_70":-5},{"_15":160,"_29":71,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":161,"_39":-5,"_40":162,"_42":163,"_44":164,"_165":166,"_167":168,"_169":-5,"_170":171,"_172":-5,"_128":-5,"_173":-5,"_69":71,"_70":-5},"tech_oa_T10057","{\"taxonomy_id\":\"tax_rd_T10057\"}","openalex_topics","2026-05-17T09:00:01.289064261","2026-05-17T09:00:01.289066518","techType","concept","domain","Physical Sciences","proposedYear","maturityLevel","growing","wikipediaUrl","patentCount","tech_oa_T12111","{\"taxonomy_id\":\"tax_rd_T12111\"}","2026-05-17T09:00:10.198971345","2026-05-17T09:00:10.198973847",{"_15":205,"_29":71,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":206,"_39":-5,"_40":41,"_42":207,"_44":208,"_185":186,"_187":-5,"_188":189,"_190":209,"_192":210,"_194":95,"_195":196,"_197":95,"_198":-5,"_69":71,"_70":-5},{"_15":199,"_29":73,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":200,"_39":-5,"_40":41,"_42":201,"_44":202,"_185":186,"_187":-5,"_188":189,"_190":203,"_192":204,"_194":95,"_195":196,"_197":95,"_198":-5,"_69":73,"_70":-5},{"_15":181,"_29":72,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":182,"_39":-5,"_40":41,"_42":183,"_44":184,"_185":186,"_187":-5,"_188":189,"_190":191,"_192":193,"_194":95,"_195":196,"_197":95,"_198":-5,"_69":72,"_70":-5},"tax_rd_T10500","{\"openalex_topic\":\"T10500\"}","2026-05-14T00:30:27.19960051","2026-05-23T12:39:17.252016307","taxonomyType","RESEARCH_DOMAIN","code","level",3,"parentId","tax_rd_2206","path","Physical Sciences/Engineering/Computational Mechanics/Sparse and Compressive Sensing Techniques","sortOrder","isLeaf",true,"entityCount","children","tax_rd_T12111","{\"openalex_topic\":\"T12111\"}","2026-05-14T00:28:40.240169019","2026-05-23T11:52:48.310419885","tax_rd_2209","Physical Sciences/Engineering/Industrial and Manufacturing Engineering/Industrial Vision Systems and Defect Detection","tax_rd_T10057","{\"openalex_topic\":\"T10057\"}","2026-05-14T00:27:16.297166939","2026-05-23T12:39:32.248012328","tax_rd_1707","Physical Sciences/Computer Science/Computer Vision and Pattern Recognition/Face and Expression Recognition",{"_74":212,"_76":213},[],[214],{"_79":215,"_81":216,"_83":217,"_85":218},1781173735821,{"_88":220,"_90":91,"_92":215,"_93":-5,"_94":95,"_96":95,"_97":95,"_98":-5,"_99":-5,"_100":68,"_35":101,"_102":103},[87,219],"[\"portal\",\"industries\"]","industries",[221,222,223,224,225,226,227,228,229,230,231,232],{"_15":284,"_29":285,"_31":-5,"_32":286,"_33":287,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":288,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":285,"_70":287},{"_15":280,"_29":281,"_31":-5,"_32":-5,"_33":282,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":283,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":281,"_70":282},{"_15":274,"_29":275,"_31":-5,"_32":276,"_33":277,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":278,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":275,"_70":277},{"_15":270,"_29":271,"_31":-5,"_32":-5,"_33":272,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":273,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":271,"_70":272},{"_15":266,"_29":267,"_31":-5,"_32":-5,"_33":268,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":269,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":267,"_70":268},{"_15":260,"_29":261,"_31":-5,"_32":262,"_33":263,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":264,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":261,"_70":263},{"_15":256,"_29":257,"_31":-5,"_32":-5,"_33":258,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":259,"_194":95,"_195":196,"_197":95,"_198":-5,"_69":257,"_70":258},{"_15":252,"_29":253,"_31":-5,"_32":-5,"_33":254,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":255,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":253,"_70":254},{"_15":248,"_29":249,"_31":-5,"_32":-5,"_33":250,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":251,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":249,"_70":250},{"_15":244,"_29":245,"_31":-5,"_32":-5,"_33":246,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":247,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":245,"_70":246},{"_15":240,"_29":241,"_31":-5,"_32":-5,"_33":242,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":243,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":241,"_70":242},{"_15":233,"_29":234,"_31":-5,"_32":-5,"_33":235,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":236,"_42":-5,"_44":-5,"_185":237,"_187":-5,"_188":91,"_190":238,"_192":239,"_194":95,"_195":68,"_197":95,"_198":-5,"_69":234,"_70":235},"tax_ind_smartphone","智能手机","Smartphone","manual","INDUSTRY","tax_ind_root","科技行业/智能手机","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算","tax_ind_blockchain","区块链",[265],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[279],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[289,290],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_293":24},"SITE_BASE_URL"]

Learning <i>k</i> for kNN Classification 论文

Learning <i>k</i> for kNN Classification · 相关技术

相关技术