This model was obtained by modifying the regularization term mostly known as the Dirichlet regularization term given by ∫ Ω | ∇ u ( x ) | 2 d x, (9).
Therefore, this sparsity on the phase errors is incorporated into the problem by using the regularization term ∥β−1∥1.
By integrating the regularization term into the standard back-projection framework, we solve a least squares minimization problem to seek the desired high-resolution (HR) image.
Therefore, this group sparsity nature on the phase errors is incorporated into the problem by using the regularization term (sum ^{I}_{i=1}left (sum ^{M}_{m=1}left |mathbf {Q}(i,m -1 right |^{2}right)^{1/2}).
By introducing the regularization term and replacing 2-norm of slack variables instead of 1-norm, our RKNNWTSVR only needs to solve a simple system of linear equations with low computational cost, and at the same time, it improves the generalization performance.
An iterative regularization model (IRM) [13], which replaces the regularization term by a generalized Bregman distance [14, 15], was proposed.
In Fig. 3, we demonstrate the impact of the regularization in the classical model, Eq. (20), in the top rows, and in the natural model, Eq. (21), in the bottom rows, by applying a different scaling in the regularization term, (B=0.06), 0.3, and 0.6, respectively.
The strength of the regularization term, determined by λ, was chosen carefully.
The regularization term is weighted by a constant β representing the proportion of reads that have to be additionally explained by an allele combination to choose a heterozygous solution over a homozygous one.
