{"title":"人脸识别的邻域判别最近邻特征线分析","authors":"Lijun Yan, Jeng-Shyang Pan, S. Chu, J. Roddick","doi":"10.1109/IBICA.2011.91","DOIUrl":null,"url":null,"abstract":"A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.","PeriodicalId":158080,"journal":{"name":"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition\",\"authors\":\"Lijun Yan, Jeng-Shyang Pan, S. Chu, J. Roddick\",\"doi\":\"10.1109/IBICA.2011.91\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.\",\"PeriodicalId\":158080,\"journal\":{\"name\":\"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IBICA.2011.91\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IBICA.2011.91","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition
A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.
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