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These models are obtained by expanding high‐order Volterra kernels, viewed as symmetric tensors, by means of symmetric or doubly symmetric PARAFAC decompositions[34, 35].

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view.

Moreover, we preprocessed the features by min-max normalization before employing the RBF kernel on each view.

Besides the MOR model, we also explored the use of SVM plus fisher kernel for multi-view object detection.

A weakly singular kernel has been viewed as an important case in this study.

As shown later, the diffusion kernel can be viewed as a discretization of the Gaussian kernel.

This kernel can be viewed as a discretization of the Gaussian kernel.

This pairwise kernel can be viewed as the weighted adjacency matrix of a Kronecker product graph of two graphs associated with the constituent kernels [ 6].

From the Bayesian point of view, the kernel reflects a priori information about the target system, which implies that the information obtained from I/O data differs over kernels.

The operator T is called the Hilbert-type operator and the function (k x,y)) is called the symmetric kernel of T. In view of this point, Hilbert's inequality (1) can be expressed by (Ta, b leqfrac{pi}{sin(frac{pi}{p})} |a|_{p} |b|_{q}, where the kernel (k x,y)=frac{1}{x+y}) and the formal inner product ((Ta, b)) between Ta and b is given by ((Ta, b):= sum_{n=1}^{infty}(Ta)(n b_{n}).

From this point of view, graph kernels provide a nice framework combining machine learning and graph theory techniques.