Near oracle performance and block analysis of signal space greedy methods

• Raja Giryes ^[1] ; Deanna Needell ^[2]
  1. [1] Duke University

     Duke University

     Township of Durham, Estados Unidos

     
  2. [2] Claremont McKenna College

     Claremont McKenna College

     Estados Unidos

     
• Localización: Journal of approximation theory, ISSN 0021-9045, Vol. 194, Nº 1 (June 2015), 2015, págs. 157-174
• Idioma: inglés
• DOI: 10.1016/j.jat.2015.02.007
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• Resumen

  • Compressive sampling (CoSa) is a new methodology which demonstrates that sparse signals can be recovered from a small number of linear measurements. Greedy algorithms like CoSaMP have been designed for this recovery, and variants of these methods have been adapted to the case where sparsity is with respect to some arbitrary dictionary rather than an orthonormal basis. In this work we present an analysis of the so-called Signal Space CoSaMP method when the measurements are corrupted with mean-zero white Gaussian noise. We establish near-oracle performance for recovery of signals sparse in some arbitrary dictionary. In addition, we analyze the block variant of the method for signals whose supports obey a block structure, extending the method into the model-based compressed sensing framework. Numerical experiments confirm that the block method significantly outperforms the standard method in these settings.