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

Liming Wangy, Jiaji Huang, Xin Yuan, Volkan Cevher, Miguel Rodrigues, Robert Calderban, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Information Theory - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781467377041
StatePublished - Sep 28 2015
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09


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