{"title":"人脸识别中的最近邻回归","authors":"Shu Yang, Chao Zhang","doi":"10.1109/ICPR.2006.989","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Regression Nearest Neighbor in Face Recognition\",\"authors\":\"Shu Yang, Chao Zhang\",\"doi\":\"10.1109/ICPR.2006.989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets\",\"PeriodicalId\":236033,\"journal\":{\"name\":\"18th International Conference on Pattern Recognition (ICPR'06)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"18th International Conference on Pattern Recognition (ICPR'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2006.989\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th International Conference on Pattern Recognition (ICPR'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2006.989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets