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end{aligned}Degenerate kernel methods are crucial in approximation theory and in scientific computing.

It shows that the kernel methods are more robust to noise than the linear methods.

Subspaces constructed on the basis of kernel methods are also suitable for approximating nonlinear texture features in target images.

Several inpainting methods in which kernel methods are introduced into PCA-based subspace construction have also been proposed[32 35].

Thus, for highly nonlinear data distribution, these kernel methods are more suitable for low-resolution and illumination variation conditions.

Owing to high computational efficiency and nonlinear projection ability of using kernel functions, kernel methods are applied to extend the traditional linear model for feature selection and fusion.

fitting point kernel methods were done for DLEBF computation.

The success of kernel methods is very much dependent on the choice of kernels.

Implementation aspects of distributed and asynchronous non-linear kernel methods were addressed in [1].

The power of kernel methods is that we do not have to compute ϕ(x i ) explicitly.

One of the important properties of kernel methods is that they can be applied to any kind of input domain, for which it is possible to construct an appropriate kernel.