Sparsity estimation from compressive projections via sparse random matrices

Sparsity estimation from compressive projections via sparse random matrices

Abstract The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. We consider both the noise-free and the noisy settings, and we show how to extend the proposed framework to the case of non-exactly sparse...

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Bibliographic Details
Main Authors: Chiara Ravazzi, Sophie Fosson, Tiziano Bianchi, Enrico Magli
Format: Article
Language:English
Published: SpringerOpen 2018-09-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Sparsity recovery
Compressed sensing
High-dimensional statistical inference
Gaussian mixture models
Maximum likelihood
Sparse random matrices
Online Access:http://link.springer.com/article/10.1186/s13634-018-0578-0

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http://link.springer.com/article/10.1186/s13634-018-0578-0

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