Sentence examples for value shrinkage from inspiring English sources

In the simplest case, with an isotropic noise model, our procedure is equivalent to a classical singular value shrinkage estimator.

For non-isotropic noise models, however, our method does not reduce to singular value shrinkage, and instead yields new estimators that perform well in experiments.

The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization.

We use optimal singular value shrinkage denoising algorithm (OptShrink), a data-driven method, recently used for denoising of low-rank matrix [31].

The singular value shrinkage operator for matrix Z is defined as ( {mathcal{D}}_{alpha }(Z)=U{S}_{alpha}left varSigma right){V}^T ), where S α is soft-thresholding operator for matrix Σ with respect to α.

D μ represents the "singular value shrinkage operator": let us denote the singular value decomposition (SVD) of a matrix Θ as Θ =  UΣV T, then D μ  =  UD μ VT.

As can be seen in this figure, different model will give different value of shrinkage stress with the higher shrinkage stress is obtained from the use of Eq. (8.b) or model of Baluch et al. followed by Eq. (1.c), and then Eq. (5.a) or model of Silfwerbrand and Eq. (6.a) or model of Carlsward.

The 10%% overestimate value of shrinkage stress by Eq. (8.b) could be tolerated.

The maximum value of shrinkage% was 12.6 at height +12 and depth 3.

Based on the Australian Standard AS3600 provisions the acceptable value for shrinkage required a value below 0.075 % at the age 56 days.

Positive values of g indicate population growth, while negative values indicate shrinkage.