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An Information Theoretic Study for Noisy Compressed Sensing With Joint Sparsity Model-2
Information Theory · Submitted 4 Apr 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction.
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The abstract, sentence by sentence
In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal to noise ratio, the number of measurement vectors and the number of measurements. These bounds can be used to provide a guideline to determine the system parameters in various applications of compressed sensing with noisy JSM-2. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to give theoretical explanations about the benefits enabled by joint sparsity structure in noisy JSM-2. We compare our sufficient condition with the existing result of noisy multiple measurement vectors model (MMV). As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. | background 0.54 | 0.49 | 0.04 | asserted 0.92 |
| s1 | Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal to noise ratio, the number of measurement vectors and the number of measurements. | contribution 0.47 | 0.96 | 0.08 | asserted 1.00 |
| s2 | These bounds can be used to provide a guideline to determine the system parameters in various applications of compressed sensing with noisy JSM-2. | interpretation 0.54 | 0.64 | 0.07 | asserted 0.65 |
| s3 | Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. | contribution 0.59 | 0.97 | 0.15 | asserted 1.00 |
| s4 | We interpret these conditions to give theoretical explanations about the benefits enabled by joint sparsity structure in noisy JSM-2. | interpretation 1.00 | 0.93 | 0.09 | asserted 0.90 |
| s5 | We compare our sufficient condition with the existing result of noisy multiple measurement vectors model (MMV). | contribution 0.61 | 0.83 | 0.05 | asserted 0.69 |
| s6 | As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction. | result 0.99 | 0.94 | 0.59 | hedged possibility 0.93 |
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The paper
Original abstract
In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal to noise ratio, the number of measurement vectors and the number of measurements. These bounds can be used to provide a guideline to determine the system parameters in various applications of compressed sensing with noisy JSM-2. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to give theoretical explanations about the benefits enabled by joint sparsity structure in noisy JSM-2. We compare our sufficient condition with the existing result of noisy multiple measurement vectors model (MMV). As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction.
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