{"title":"鲁棒稀疏性的非凸p范数投影","authors":"Mithun Das Gupta, Sanjeev Kumar","doi":"10.1109/ICCV.2013.201","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate the properties of Lp norm (p ≤1) within a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for Lp norm projection on the non-negative simplex. We compare with L1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification proposed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Non-convex P-Norm Projection for Robust Sparsity\",\"authors\":\"Mithun Das Gupta, Sanjeev Kumar\",\"doi\":\"10.1109/ICCV.2013.201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we investigate the properties of Lp norm (p ≤1) within a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for Lp norm projection on the non-negative simplex. We compare with L1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification proposed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.\",\"PeriodicalId\":6351,\"journal\":{\"name\":\"2013 IEEE International Conference on Computer Vision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCV.2013.201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCV.2013.201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we investigate the properties of Lp norm (p ≤1) within a projection framework. We start with the KKT equations of the non-linear optimization problem and then use its key properties to arrive at an algorithm for Lp norm projection on the non-negative simplex. We compare with L1 projection which needs prior knowledge of the true norm, as well as hard thresholding based sparsification proposed in recent compressed sensing literature. We show performance improvements compared to these techniques across different vision applications.