噪声低等级列式传感

arXiv - EE - Signal Processing Pub Date : 2024-09-12 DOI:arxiv-2409.08384

Ankit Pratap Singh, Namrata Vaswani

{"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":null,"pages":null},"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\":null,\"pages\":null},\"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}

引用次数: 0

摘要

这篇文章研究了解决有噪声低秩列智传感（LRCS）问题的 AltGDmin 算法。我们的样本复杂度保证在现有最佳保证的基础上提高了一个因子 $\max(r,\log(1/\epsilon))/r$，其中 $r$ 是未知矩阵的秩，$\epsilon$ 是最终期望的精确度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Noisy Low Rank Column-wise Sensing

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

arXiv - EE - Signal Processing

自引率

0.00%

发文量