Sentence examples for sparsity with from inspiring English sources

The SAMP algorithm uses an iterative method to estimate the signal sparsity, with a fixed step size to be used in each stage.

The new method combines novel concepts of group sparsity with contiguity-constrained clusterization to produce physiologically consistent regions of interest in illustrative fMRI data whose causal interactions may then be easily estimated, something impossible under the usual ICA assumptions.

We then compare the performance of the support recovery rate and the MSE of the recovered signal using 1) OMP with sparsity k known; 2) OMP with unknown sparsity with stopping rule of ∥ r t ∥ ℓ 2 < ∥ n ∥ ℓ 2 as proposed in [14]; 3) DOMP with different false alarm probabilities, i.e., P FA =0.05, P FA =0.01, and P FA =0.001.

However, this 'divide-and-conquer' approach leads to data sparsity, with the consequence that it suffers from poor generalization, meaning that it is unable to accurately predict parameters for models of unseen contexts: the hard decision tree is a weak function approximator.

The main problems with PMM are related to donor sparsity – with few donors in the vicinity of an incomplete case, the imputed values may lead to bias.

Setting α to 0.5 leads to a solution of medium sparsity with 31 non-zero elements (Additional file 1: Figure S1).

Fig. 3 The MSE versus μ1,μ2 for different sparsities with N=M=800, L=4, β=1.5, and SNR = 25 dB.

Furthermore, we also simulate the MSE versus SNR for different sparsities with N=M=800, L=4, β=1.5, μ1=0.03, and μ2=0.26, as shown in Fig. 4.

In summary, these simulation results verify the validity of the designed LCS signatures based on Theorem 1. Fig. 2 μ(B) versus μ1 and μ2 with N=M=800, β=1.5, and L=4. Figure 3 shows the MSE versus μ1,μ2 for different sparsities with N=M=800, L=4, β=1.5, and SNR = 25 dB.

Among MR applications, CE-MRA is particularly well suited for CS acceleration because of the inherent image domain sparsity associated with bright vessels with sharp edges superimposed on dark background tissue.

The optimum λ determined at the corner decreases with the decrease in p. The reconstructed μ a distributions are shown in Fig. 2 with the same color scale for all images using (a) Tikhonov regularization with p = 2 and (b) to (d) the l p sparsity regularization with p = 1, 1/2 and 1/4, respectively.