A concentration-of-measure inequality for multiple-measurement models
© 2015 IEEE. 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.
Wangy, L; Huang, J; Yuan, X; Cevher, V; Rodrigues, M; Calderban, R; Carin, L
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International Standard Book Number 13 (ISBN-13)
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