A concentration-of-measure inequality for multiple-measurement models

Conference Paper

Classical compressive sensing typically assumes a single measurement, and theoretical analysis often relies on corresponding concentration-of-measure results. There are many real-world applications involving multiple compressive measurements, from which the underlying signals may be estimated. In this paper, we establish a new concentration-of-measure inequality for a block-diagonal structured random compressive sensing matrix with Rademacher-ensembles. We discuss applications of this newly-derived inequality to two appealing compressive multiple-measurement models: for Gaussian and Poisson systems. In particular, Johnson-Lindenstrauss-type results and a compressed-domain classification result are derived for a Gaussian multiple-measurement model. We also propose, as another contribution, theoretical performance guarantees for signal recovery for multi-measurement Poisson systems, via the inequality.

Full Text

Duke Authors

Cited Authors

  • Wangy, L; Huang, J; Yuan, X; Cevher, V; Rodrigues, M; Calderban, R; Carin, L

Published Date

  • September 28, 2015

Published In

Volume / Issue

  • 2015-June /

Start / End Page

  • 2341 - 2345

International Standard Serial Number (ISSN)

  • 2157-8095

International Standard Book Number 13 (ISBN-13)

  • 9781467377041

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2015.7282874

Citation Source

  • Scopus