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Proceedings Paper

Active learning versus compressive sampling
Author(s): Rui Castro; Jarvis Haupt; Robert Nowak
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Paper Abstract

Compressive sampling (CS), or Compressed Sensing, has generated a tremendous amount of excitement in the signal processing community. Compressive sampling, which involves non-traditional samples in the form of randomized projections, can capture most of the salient information in a signal with a relatively small number of samples, often far fewer samples than required using traditional sampling schemes. Adaptive sampling (AS), also called Active Learning, uses information gleaned from previous observations (e.g., feedback) to focus the sampling process. Theoretical and experimental results have shown that adaptive sampling can dramatically outperform conventional (non-adaptive) sampling schemes. This paper compares the theoretical performance of compressive and adaptive sampling for regression in noisy conditions, and it is shown that for certain classes of piecewise constant signals and high SNR regimes both CS and AS are near optimal. This result is remarkable since it is the first evidence that shows that compressive sampling, which is non-adaptive, cannot be significantly outperformed by any other method (including adaptive sampling procedures), even in the presence of noise. The performance of CS schemes for signal detection is also investigated.

Paper Details

Date Published: 4 May 2006
PDF: 10 pages
Proc. SPIE 6232, Intelligent Integrated Microsystems, 623208 (4 May 2006); doi: 10.1117/12.669725
Show Author Affiliations
Rui Castro, Rice Univ. (United States)
Univ. of Wisconsin-Madison (United States)
Jarvis Haupt, Univ. of Wisconsin-Madison (United States)
Robert Nowak, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 6232:
Intelligent Integrated Microsystems
Ravindra A. Athale; John C. Zolper, Editor(s)

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