{"title":"利用正交匹配追踪恢复稀疏信号的一个新结果","authors":"Xueping Chen, Jianzhong Liu, Jiandong Chen","doi":"10.1080/24754269.2022.2048445","DOIUrl":null,"url":null,"abstract":"Orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. In this paper, by exploiting the Wielandt inequality and some properties of orthogonal projection matrix, we obtained a new number of iterations required for the OMP algorithm to perform exact recovery of sparse signals, which improves significantly upon the latest results as we know.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"220 - 226"},"PeriodicalIF":0.7000,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A new result on recovery sparse signals using orthogonal matching pursuit\",\"authors\":\"Xueping Chen, Jianzhong Liu, Jiandong Chen\",\"doi\":\"10.1080/24754269.2022.2048445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. In this paper, by exploiting the Wielandt inequality and some properties of orthogonal projection matrix, we obtained a new number of iterations required for the OMP algorithm to perform exact recovery of sparse signals, which improves significantly upon the latest results as we know.\",\"PeriodicalId\":22070,\"journal\":{\"name\":\"Statistical Theory and Related Fields\",\"volume\":\"6 1\",\"pages\":\"220 - 226\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2022-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Theory and Related Fields\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1080/24754269.2022.2048445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Theory and Related Fields","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/24754269.2022.2048445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
A new result on recovery sparse signals using orthogonal matching pursuit
Orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. In this paper, by exploiting the Wielandt inequality and some properties of orthogonal projection matrix, we obtained a new number of iterations required for the OMP algorithm to perform exact recovery of sparse signals, which improves significantly upon the latest results as we know.
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