Math @ Duke
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Publications [#265131] of Guillermo Sapiro
Papers Published
- Carin, L; Baraniuk, RG; Cevher, V; Dunson, D; Jordan, MI; Sapiro, G; Wakin, MB, Learning Low-Dimensional Signal Models: A Bayesian approach based on incomplete measurements.,
IEEE signal processing magazine, vol. 28 no. 2
(March, 2011),
pp. 39-51, ISSN 1053-5888 [doi]
(last updated on 2025/02/02)
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. © 2006 IEEE.
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