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We compare the reconstruction performance among the SKP measurement matrix, the random Gaussian measurement matrix and random Bernoulli measurement matrix.

Both simulations and experiments on real data demonstrate that the proposed algorithm outperforms the ℓ1-SVD algorithm, which exploits straightforwardly ℓ2,1 minimization, for both deterministic basis matrix and random basis matrix.

We provided experimental results to testify that, for both deterministic basis matrix and random basis matrix, the proposed SW ℓ2,1-SVD algorithm can obtain better performance than that of the ℓ1-SVD algorithm with fewer number of snapshots and lower SNR.

We calculated the odds of being overweight (BMI z-score ≥ 85th percentile) according to BPA concentration using generalized linear mixed models with an unstructured correlation matrix and random intercept.

We used linear mixed models with an unstructured covariance matrix and random intercept to estimate the association between maternal urinary phthalate metabolite concentrations at 16 weeks and the two repeated maternal blood pressure measures collected at <20 weeks gestation.

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Figs. 1 and 2 demonstrate that the SKP measurement matrix outperforms the random Gaussian measurement matrices and random Bernoulli measurement matrices at the compression ratio of 0.5.

Simulation results show that the reconstruction performance of the SKP measurement matrices is superior to that of the random Gaussian measurement matrices and random Bernoulli measurement matrices.

The reconstructed images by the random Gaussian measurement matrices and random Bernoulli measurement matrices are blurry and lose some details compared to the SKP measurement matrix.

When the size of the pascal matrix is 8, the reconstruction performance of the SKP measurement matrices has a serious decline or even less than the random Gaussian measurement matrices and random Bernoulli measurement matrices, which is caused by the further weakening of the orthogonality between row vectors of the SKP measurement matrices.

It can be seen from Table 1 when the size of the pascal matrix is 2 or 4, the reconstruction property of the SKP measurement matrices is better than that of the random Gaussian measurement matrices and random Bernoulli measurement matrices from PSNR values.

In order to acquire the reconstruction performance of reconstruction algorithms under different measurement matrices, the measurement signals are obtained by 1000-fold cross-validations using different measurement matrices, Gaussian random measurement matrix, and Bernoulli random measurement matrix.

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