Learning low-dimensional signal models

Lawrence Carin; Richard Baraniuk; Volkan Cevher; David Dunson; Michael Jordan; Guillermo Sapiro; Michael Wakin

doi:10.1109/MSP.2010.939733

Learning low-dimensional signal models

Lawrence Carin
, Richard Baraniuk
, Volkan Cevher
, David Dunson
, Michael Jordan
, Guillermo Sapiro
, Michael Wakin

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

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.

Original language	English (US)
Article number	5714381
Pages (from-to)	39-51
Number of pages	13
Journal	IEEE Signal Processing Magazine
Volume	28
Issue number	2
DOIs	https://doi.org/10.1109/MSP.2010.939733
State	Published - Mar 2011
Externally published	Yes

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Keywords

Analytical models
Bayesian methods
Data models
Manifolds
Monitoring
Training data

Access to Document

10.1109/MSP.2010.939733

Cite this

@article{0c3b42c26d374f249468cb460bff34bb,

title = "Learning low-dimensional signal models",

abstract = "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.",

keywords = "Analytical models, Bayesian methods, Data models, Manifolds, Monitoring, Training data",

author = "Lawrence Carin and Richard Baraniuk and Volkan Cevher and David Dunson and Michael Jordan and Guillermo Sapiro and Michael Wakin",

year = "2011",

month = mar,

doi = "10.1109/MSP.2010.939733",

language = "English (US)",

volume = "28",

pages = "39--51",

journal = "IEEE Signal Processing Magazine",

issn = "1053-5888",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

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

PY - 2011/3

Y1 - 2011/3

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.

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.

KW - Analytical models

KW - Bayesian methods

KW - Data models

KW - Manifolds

KW - Monitoring

KW - Training data

UR - https://www.scopus.com/pages/publications/85032751900

UR - https://www.scopus.com/pages/publications/85032751900#tab=citedBy

U2 - 10.1109/MSP.2010.939733

DO - 10.1109/MSP.2010.939733

M3 - Article

C2 - 24363544

AN - SCOPUS:85032751900

SN - 1053-5888

VL - 28

SP - 39

EP - 51

JO - IEEE Signal Processing Magazine

JF - IEEE Signal Processing Magazine

IS - 2

M1 - 5714381

ER -

Learning low-dimensional signal models

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this