Loading [MathJax]/extensions/MathZoom.js
A concentration-of-measure inequality for multiple-measurement models | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Classical compressive sensing typically assumes a single measurement, and theoretical analysis often relies on corresponding concentration-of-measure results. There are m...Show More

Abstract:

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.
Date of Conference: 14-19 June 2015
Date Added to IEEE Xplore: 01 October 2015
ISBN Information:

ISSN Information:

Conference Location: Hong Kong, China

Contact IEEE to Subscribe

References

References is not available for this document.