Uniform Uncertainty Principle and signal recovery via Regularized Orthogonal Matching Pursuit

Abstract

This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery from an incomplete set of linear measurements -- L_1-minimization methods and iterative methods (Matching Pursuits). We find a simple regularized version of the Orthogonal Matching Pursuit (ROMP) which has advantages of both approaches: the speed and transparency of OMP and the strong uniform guarantees of the L_1-minimization. Our algorithm ROMP reconstructs a sparse signal in a number of iterations linear in the sparsity (in practice even logarithmic), and the reconstruction is exact provided the linear measurements satisfy the Uniform Uncertainty Principle.

This is the final version of the paper, including referee suggestions

References in corpus (2)

• Algorithmic linear dimension reduction in the l_1 norm for sparse vectors
• Uncertainty Principles and Vector Quantization

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