Online Estimation of Coherent Subspaces with Adaptive Sampling

Greg Ongie, David Hong, Dejiao Zhang, L. Balzano
{"title":"Online Estimation of Coherent Subspaces with Adaptive Sampling","authors":"Greg Ongie, David Hong, Dejiao Zhang, L. Balzano","doi":"10.1109/SSP.2018.8450830","DOIUrl":null,"url":null,"abstract":"This work investigates adaptive sampling strategies for online subspace estimation from streaming input vectors where the underlying subspace is coherent, i.e., aligned with some subset of the coordinate axes. We adapt the previously proposed Grassmannian rank-one update subspace estimation (GROUSE) algorithm to incorporate an adaptive sampling strategy that substantially improves over uniform random sampling. Our approach is to sample some proportion of the entries based on the leverage scores of the current subspace estimate. Experiments on synthetic data demonstrate that the adaptive measurement scheme greatly improves the convergence rate of GROUSE over uniform random measurements when the underlying subspace is coherent.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This work investigates adaptive sampling strategies for online subspace estimation from streaming input vectors where the underlying subspace is coherent, i.e., aligned with some subset of the coordinate axes. We adapt the previously proposed Grassmannian rank-one update subspace estimation (GROUSE) algorithm to incorporate an adaptive sampling strategy that substantially improves over uniform random sampling. Our approach is to sample some proportion of the entries based on the leverage scores of the current subspace estimate. Experiments on synthetic data demonstrate that the adaptive measurement scheme greatly improves the convergence rate of GROUSE over uniform random measurements when the underlying subspace is coherent.
自适应采样的相干子空间在线估计
这项工作研究了从流输入向量在线子空间估计的自适应采样策略,其中底层子空间是相干的,即与坐标轴的某些子集对齐。我们采用了先前提出的格拉斯曼秩一更新子空间估计(GROUSE)算法,纳入了一种自适应采样策略,该策略大大改进了均匀随机采样。我们的方法是根据当前子空间估计的杠杆分数对条目的某些比例进行抽样。在综合数据上的实验表明,当底层子空间为相干时,自适应测量方案大大提高了GROUSE对均匀随机测量的收敛速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信