Learning low-dimensional signal models

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

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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 languageEnglish (US)
Article number5714381
Pages (from-to)39-51
Number of pages13
JournalIEEE Signal Processing Magazine
Issue number2
StatePublished - Mar 2011


  • Analytical models
  • Bayesian methods
  • Data models
  • Manifolds
  • Monitoring
  • Training data

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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