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      • Department of Electrical and Electronics Engineering
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      An empirical eigenvalue-threshold test for sparsity level estimation from compressed measurements

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      Author
      Lavrenko, A.
      Römer, F.
      Del Galdo, G.
      Thoma, R.
      Arıkan, Orhan
      Date
      2014
      Source Title
      Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
      Print ISSN
      2219-5491
      Publisher
      IEEE
      Pages
      1761 - 1765
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Compressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge of the sparsity level, i.e., the number of non-zero elements. However, the sparsity level is often not known a priori and can even vary with time. In this contribution we show that it is possible to estimate the sparsity level directly in the compressed domain, provided that multiple independent observations are available. In fact, one can use classical model order selection algorithms for this purpose. Nevertheless, due to the influence of the measurement process they may not perform satisfactorily in the compressed sensing setup. To overcome this drawback, we propose an approach which exploits the empirical distributions of the noise eigenvalues. We demonstrate its superior performance compared to state-of-the-art model order estimation algorithms numerically.
      Keywords
      Detection
      Eigenvalues and eigenfunctions
      Error detection
      Mathematical models
      Signal processing
      Signal reconstruction
      Compressed domain
      Computationally efficient
      Empirical distributions
      Measurement process
      Model-order selection
      Signal of interests
      Sparsity level
      Compressed sensing
      Permalink
      http://hdl.handle.net/11693/27496
      Published Version (Please cite this version)
      https://doi.org/10.5281/zenodo.44108
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      • Department of Electrical and Electronics Engineering 3524
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