{"title":"基于改进最大切缘准则的降维肿瘤分类","authors":"Shanwen Zhang, Rongzhi Jing","doi":"10.1109/ICIC.2011.148","DOIUrl":null,"url":null,"abstract":"Based on Maximum margin criterion (MMC), a new algorithm, named modified MMC, is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and aims at maximizing the average margin between classes in the projected space. After projecting, the considered pair wise points within the same class are as close as possible, while those between different classes are as far as possible. The performance on two gene expression profiles datasets demonstrates the effectiveness of the proposed method.","PeriodicalId":6397,"journal":{"name":"2011 Fourth International Conference on Information and Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Dimension Reduction Based on Modified Maximum Margin Criterion for Tumor Classification\",\"authors\":\"Shanwen Zhang, Rongzhi Jing\",\"doi\":\"10.1109/ICIC.2011.148\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on Maximum margin criterion (MMC), a new algorithm, named modified MMC, is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and aims at maximizing the average margin between classes in the projected space. After projecting, the considered pair wise points within the same class are as close as possible, while those between different classes are as far as possible. The performance on two gene expression profiles datasets demonstrates the effectiveness of the proposed method.\",\"PeriodicalId\":6397,\"journal\":{\"name\":\"2011 Fourth International Conference on Information and Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Fourth International Conference on Information and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIC.2011.148\",\"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 Fourth International Conference on Information and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIC.2011.148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dimension Reduction Based on Modified Maximum Margin Criterion for Tumor Classification
Based on Maximum margin criterion (MMC), a new algorithm, named modified MMC, is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and aims at maximizing the average margin between classes in the projected space. After projecting, the considered pair wise points within the same class are as close as possible, while those between different classes are as far as possible. The performance on two gene expression profiles datasets demonstrates the effectiveness of the proposed method.