Learning Low-Dimensional Signal Models

被引：17

作者：

Carin, Lawrence ^{[1
]}

Baraniuk, Richard G. ^{[2
]}

Cevher, Volkan ^{[2
,3
]}

Dunson, David ^{[4
,10
]}

Jordan, Michael I. ^{[5
,6
]}

Sapiro, Guillermo

Wakin, Michael B. ^{[7
,8
,9
]}

机构：

[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27706 USA

[2] Rice Univ, Houston, TX USA

[3] Univ Maryland, College Pk, MD 20742 USA

[4] Amer Stat Assoc, Alexandria, VA USA

[5] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

[6] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA

[7] CALTECH, Pasadena, CA 91125 USA

[8] Univ Michigan, Ann Arbor, MI 48109 USA

[9] Colorado Sch Mines, Div Engn, Golden, CO 80401 USA

[10] Harvard Univ, Cambridge, MA 02138 USA

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2011年 / 28卷 / 02期

基金：

美国国家科学基金会;

关键词：

RESTRICTED ISOMETRY PROPERTY; SAMPLING METHODS;

D O I：

10.1109/MSP.2010.939733

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Sampling, coding, and streaming even the most essential data, e.g., in medical imaging and weather-monitoring applications, produce a data deluge that severely stresses the available analog-to-digital converter, communication bandwidth, and digital-storage resources. Surprisingly, while the ambient data dimension is large in many problems, the relevant information in the data can reside in a much lower dimensional space. © 2006 IEEE.

引用

页码：39 / 51

页数：13

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