TY - JOUR
T1 - Learning low-dimensional signal models
AU - Carin, Lawrence
AU - Baraniuk, Richard
AU - Cevher, Volkan
AU - Dunson, David
AU - Jordan, Michael
AU - Sapiro, Guillermo
AU - Wakin, Michael
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/1/1
Y1 - 2011/1/1
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/5714381/
UR - http://www.scopus.com/inward/record.url?scp=85032751900&partnerID=8YFLogxK
U2 - 10.1109/MSP.2010.939733
DO - 10.1109/MSP.2010.939733
M3 - Article
SN - 1053-5888
VL - 28
SP - 39
EP - 51
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 2
ER -