.png){kind=icon}
返回
One-Bit Compressed Sensing by Linear Programming
.png){kind=icon}
OA
AI
DOI：10.1002/cpa.21442.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We give the first computationally tractable and almost optimal solution to the problem of one-bit compressed sensing, showing how to accurately recover an s-sparse vector x is an element of R-n input amssym from the signs of O(s log(2)(n/s)) random linear measurements of x. The recovery is achieved by a simple linear program. This result extends to approximately sparse vectors x. Our result is universal in the sense that with high probability, one measurement scheme will successfully recover all sparse vectors simultaneously. The argument is based on solving an equivalent geometric problem on random hyperplane tessellations. (C) 2013 Wiley Periodicals, Inc.
Keyword:
SIGNAL RECOVERY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
1.5K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
Sentence Repetition as a Clinical Marker of Developmental Language Disorder: Evidence From Arabic
OAAI
OAAI分享收藏
查看更多内容