Sentence examples for effect of the sparsity from inspiring English sources

Similarly, exploring the effect of the sparsity level S, we do not gain much more insights over the experiments already conducted in the uncorrupted case [29].

If only a sparsity constraint is used (e.g., Evans and Meade 2012; Honma et al. 2014), the evaluation function can be written as: Eleft(boldsymbol{s};lambda right)={displaystyle sum_{i=1}^N}{left({d}_k-{displaystyle sum_{l=1}^N}{G}_{kl}{s}_lright)}^2+lambda {displaystyle sum_{l=1}^N}left|{s}_lright|, (3)where λ is a sparsity hyperparameter that controls the effect of the sparsity constraint.

Compared with (10) for the sparsity-agnostic KF, the last summand in (26) captures the effect of the sparsity-promoting penalty term on the error covariance.

All components present a limited number of proteins and side effects, which is a consequence of the sparsity of SCCA.

We have also studied the effect of the near-sparsity parameter, ε k, on the performance of our QNC scheme.

The effect of the l p sparsity regularization is validated by a phantom experiment in this section.

However, it is demonstrated that a target with a small differences in the absorption coefficient from the background may disappear due to the excessive effect of the l p sparsity regularization.

Numerical experiments are conducted to investigate the effect of the l p sparsity regularization with p = 1, 1/2, 1/4 on the image reconstruction of time-domain diffuse optical tomography and the results are compared with Tikhonov regularization which is identical to the l p sparsity regularization with p = 2.

Numerical experiments show that the l p sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets, although a target with a small absorption coefficient may disappear due to the strong effect of the l p sparsity regularization when the value of p is too small.

Moreover, the positive effect of sparsity order on the energy efficiency can be clearly seen, i.e., the sparser the spectrum, the more energy efficient the proposed scheme.

One widely used assumption which can mitigate the effects of dimensionality is the sparsity of the underlying parameters.