{"title":"最小角度回归方法在EIT图像重建中的应用","authors":"T. Rymarczyk, P. Adamkiewicz, E. Kozłowski","doi":"10.1109/IIPHDW.2018.8388350","DOIUrl":null,"url":null,"abstract":"The highly correlated predictors with each other's in linear models do not allow to determine the precisely influences of these predictors on the output variable. Directly application the least square method to estimate the unknown parameters may lead to a poor prediction. The addition of penalty depending on quantities of parameters to the least square criterion allows us to determine the biased estimators but also to reduce the variance of estimators. The Least Angle Regression was used to reconstruct the image in electrical impedance tomography.","PeriodicalId":405270,"journal":{"name":"2018 International Interdisciplinary PhD Workshop (IIPhDW)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of least angle regression methods for image reconstruction in EIT\",\"authors\":\"T. Rymarczyk, P. Adamkiewicz, E. Kozłowski\",\"doi\":\"10.1109/IIPHDW.2018.8388350\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The highly correlated predictors with each other's in linear models do not allow to determine the precisely influences of these predictors on the output variable. Directly application the least square method to estimate the unknown parameters may lead to a poor prediction. The addition of penalty depending on quantities of parameters to the least square criterion allows us to determine the biased estimators but also to reduce the variance of estimators. The Least Angle Regression was used to reconstruct the image in electrical impedance tomography.\",\"PeriodicalId\":405270,\"journal\":{\"name\":\"2018 International Interdisciplinary PhD Workshop (IIPhDW)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Interdisciplinary PhD Workshop (IIPhDW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIPHDW.2018.8388350\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Interdisciplinary PhD Workshop (IIPhDW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIPHDW.2018.8388350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of least angle regression methods for image reconstruction in EIT
The highly correlated predictors with each other's in linear models do not allow to determine the precisely influences of these predictors on the output variable. Directly application the least square method to estimate the unknown parameters may lead to a poor prediction. The addition of penalty depending on quantities of parameters to the least square criterion allows us to determine the biased estimators but also to reduce the variance of estimators. The Least Angle Regression was used to reconstruct the image in electrical impedance tomography.
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