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

23 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

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10.1109/MSP.2010.939733

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AU - Dunson, David

AU - Jordan, Michael

AU - Sapiro, Guillermo

AU - Wakin, Michael

PY - 2011/3

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KW - Analytical models

KW - Bayesian methods

KW - Data models

KW - Manifolds

KW - Monitoring

KW - Training data

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Learning low-dimensional signal models

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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