CaltechAUTHORS
Published March 2011 | Version public
Journal Article Metadata-only

Learning Low-Dimensional Signal Models

Creators

  • 1. ROR icon University of Maryland, College Park
  • 2. ROR icon University of California, Berkeley
  • 3. ROR icon University of Minnesota

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.

Additional Information

© 2011 IEEE. Date of publication: 17 February 2011. Many graduate students contributed to the ideas and results reviewed in this article. The authors particularly acknowledge the contributions of Minhua Chen, Armin Eftekhari, John Paisley, and Mingyuan Zhou. The authors also thank the reviewers for a careful reading of the original version of this article and suggestions that led to a significantly improved final article.

Additional details

Identifiers

Eprint ID
22840
Resolver ID
CaltechAUTHORS:20110314-091724983

Dates

Created
2011-03-16
Created from EPrint's datestamp field
Updated
2021-11-09
Created from EPrint's last_modified field