{"title":"压缩感知中11 -分析正则化的可辨识性和噪声鲁棒性","authors":"Nicodeme Marc, Turcu Flavius, D. Charles","doi":"10.1109/SYNASC.2015.21","DOIUrl":null,"url":null,"abstract":"We use several geometric techniques to characterize identifiability and to estimate noise robustness in the framework of l1-analysis regularization. This extends several recent theoretical results and algorithms that deal with the same issues in the less complex case of l1-synthesis regularizations.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"70 1","pages":"79-84"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Identifiability and Noise Robustness for l1-Analysis Regularizations in Compressive Sensing\",\"authors\":\"Nicodeme Marc, Turcu Flavius, D. Charles\",\"doi\":\"10.1109/SYNASC.2015.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We use several geometric techniques to characterize identifiability and to estimate noise robustness in the framework of l1-analysis regularization. This extends several recent theoretical results and algorithms that deal with the same issues in the less complex case of l1-synthesis regularizations.\",\"PeriodicalId\":6488,\"journal\":{\"name\":\"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"volume\":\"70 1\",\"pages\":\"79-84\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2015.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2015.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifiability and Noise Robustness for l1-Analysis Regularizations in Compressive Sensing
We use several geometric techniques to characterize identifiability and to estimate noise robustness in the framework of l1-analysis regularization. This extends several recent theoretical results and algorithms that deal with the same issues in the less complex case of l1-synthesis regularizations.
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