{"title":"半监督转导判别分析","authors":"Yi Li, Xuesong Yin","doi":"10.1109/ISKE.2010.5680867","DOIUrl":null,"url":null,"abstract":"When there is no sufficient labeled instances, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled instances are used to improve the performance. In this paper, we propose a dimensionality reduction method called semi-supervised TransductIve Discriminant Analysis (TIDA) which preserves the global and geometrical structure of the unlabeled instances in addition to separating labeled instances in different classes from each other. The proposed algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that TIDA is superior to many relevant dimensionality reduction methods.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"1 1","pages":"291-295"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Semi-supervised Transductive Discriminant Analysis\",\"authors\":\"Yi Li, Xuesong Yin\",\"doi\":\"10.1109/ISKE.2010.5680867\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When there is no sufficient labeled instances, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled instances are used to improve the performance. In this paper, we propose a dimensionality reduction method called semi-supervised TransductIve Discriminant Analysis (TIDA) which preserves the global and geometrical structure of the unlabeled instances in addition to separating labeled instances in different classes from each other. The proposed algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that TIDA is superior to many relevant dimensionality reduction methods.\",\"PeriodicalId\":6417,\"journal\":{\"name\":\"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering\",\"volume\":\"1 1\",\"pages\":\"291-295\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISKE.2010.5680867\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISKE.2010.5680867","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
When there is no sufficient labeled instances, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled instances are used to improve the performance. In this paper, we propose a dimensionality reduction method called semi-supervised TransductIve Discriminant Analysis (TIDA) which preserves the global and geometrical structure of the unlabeled instances in addition to separating labeled instances in different classes from each other. The proposed algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that TIDA is superior to many relevant dimensionality reduction methods.