Adapted statistical compressive sensing: Learning to sense gaussian mixture models

Journal Article

A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS. © 2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Duarte-Carvajalino, JM; Yu, G; Carin, L; Sapiro, G

Published Date

  • 2012

Published In

Start / End Page

  • 3653 - 3656

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2012.6288708