{"title":"噪声低等级列式传感","authors":"Ankit Pratap Singh, Namrata Vaswani","doi":"arxiv-2409.08384","DOIUrl":null,"url":null,"abstract":"This letter studies the AltGDmin algorithm for solving the noisy low rank\ncolumn-wise sensing (LRCS) problem. Our sample complexity guarantee improves\nupon the best existing one by a factor $\\max(r, \\log(1/\\epsilon))/r$ where $r$\nis the rank of the unknown matrix and $\\epsilon$ is the final desired accuracy.\nA second contribution of this work is a detailed comparison of guarantees from\nall work that studies the exact same mathematical problem as LRCS, but refers\nto it by different names.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Noisy Low Rank Column-wise Sensing\",\"authors\":\"Ankit Pratap Singh, Namrata Vaswani\",\"doi\":\"arxiv-2409.08384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter studies the AltGDmin algorithm for solving the noisy low rank\\ncolumn-wise sensing (LRCS) problem. Our sample complexity guarantee improves\\nupon the best existing one by a factor $\\\\max(r, \\\\log(1/\\\\epsilon))/r$ where $r$\\nis the rank of the unknown matrix and $\\\\epsilon$ is the final desired accuracy.\\nA second contribution of this work is a detailed comparison of guarantees from\\nall work that studies the exact same mathematical problem as LRCS, but refers\\nto it by different names.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.08384\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This letter studies the AltGDmin algorithm for solving the noisy low rank
column-wise sensing (LRCS) problem. Our sample complexity guarantee improves
upon the best existing one by a factor $\max(r, \log(1/\epsilon))/r$ where $r$
is the rank of the unknown matrix and $\epsilon$ is the final desired accuracy.
A second contribution of this work is a detailed comparison of guarantees from
all work that studies the exact same mathematical problem as LRCS, but refers
to it by different names.