65 research outputs found
Analyzing Weighted β_1 Minimization for Sparse Recovery With Nonuniform Sparse Models
In this paper, we introduce a nonuniform sparsity model and analyze the performance of an optimized weighted β_1 minimization over that sparsity model. In particular, we focus on a model where the entries of the unknown vector fall into two sets, with entries of each set having a specific probability of being nonzero. We propose a weighted β_1 minimization recovery algorithm and analyze its performance using a Grassmann angle approach. We compute explicitly the relationship between the system parameters-the weights, the number of measurements, the size of the two sets, the probabilities of being nonzero-so that when i.i.d. random Gaussian measurement matrices are used, the weighted β_1 minimization recovers a randomly selected signal drawn from the considered sparsity model with overwhelming probability as the problem dimension increases. This allows us to compute the optimal weights. We demonstrate through rigorous analysis and simulations that for the case when the support of the signal can be divided into two different subclasses with unequal sparsity fractions, the weighted β_1 minimization outperforms the regular β_1 minimization substantially. We also generalize our results to signal vectors with an arbitrary number of subclasses for sparsity
Exact Reconstruction Conditions for Regularized Modified Basis Pursuit
In this correspondence, we obtain exact recovery conditions for regularized
modified basis pursuit (reg-mod-BP) and discuss when the obtained conditions
are weaker than those for modified-CS or for basis pursuit (BP). The discussion
is also supported by simulation comparisons. Reg-mod-BP provides a solution to
the sparse recovery problem when both an erroneous estimate of the signal's
support, denoted by , and an erroneous estimate of the signal values on
are available.Comment: 17 page
- β¦