This paper focuses on the spectral distribution of kernel matrices related to radial basis functions. The asymptotic behaviour of eigenvalues of kernel matrices related to radial basis functions with different smoothness are studied. These results are obtained by estimated the coefficients of an orthogonal expansion of the underlying kernel function. Beside many other results, we prove that there are exactly (k+d−1/d-1) eigenvalues in the same order for analytic separable kernel functions like the Gaussian in Rd. This gives theoretical support for how to choose the diagonal scaling matrix in the RBF-QR method (Fornberg et al, SIAM J. Sci. Comput. (33), 2011) which can stably compute Gaussian radial basis function interpolants
We use methods from the Fock space and Segal–Bargmann theories to prove several results on the Gauss...
AbstractInterpolation problems for analytic radial basis functions like the Gaussian and inverse mul...
We address the local spectral behavior of the random matrix Π_1U^(⊗k)Π_2U^(⊗k∗)Π_1 where U is ...
This paper focuses on the spectral distribution of kernel matrices related to radial basis functions...
This paper focuses on the spectral distribution of kernel matrices related to radial basis functions...
This paper focuses on spectral distribution of kernel matrices related to radial basis functions. By...
We consider nxn matrices whose (i, j)th entry is f(X-i(T) X-j), where X-1,..., X-n are i.i.d. standa...
We consider random matrices whose entries are obtained by applying a (nonlinear) kernel function to ...
We consider n-by-n matrices whose (i, j)th entry is f(XTi Xj), where X1,..., Xn are i.i.d. standard ...
This paper reveals and examines the relationship between the solution and stability of Fredholm inte...
We study the spectra of p×p random matrices K with off-diagonal (i, j) entry equal to n−1/2k(XTi Xj/...
The spectral behavior of kernel matrices built from complex multi-variate data is established in the...
Kernel methods are an extremely popular set of techniques used for many important machine learning a...
In this dissertation, it is first shown that, when the radial basis function is a $p$-norm and $1 2...
We use the steepest descents method to study the integral kernel of a family of normal random matrix...
We use methods from the Fock space and Segal–Bargmann theories to prove several results on the Gauss...
AbstractInterpolation problems for analytic radial basis functions like the Gaussian and inverse mul...
We address the local spectral behavior of the random matrix Π_1U^(⊗k)Π_2U^(⊗k∗)Π_1 where U is ...
This paper focuses on the spectral distribution of kernel matrices related to radial basis functions...
This paper focuses on the spectral distribution of kernel matrices related to radial basis functions...
This paper focuses on spectral distribution of kernel matrices related to radial basis functions. By...
We consider nxn matrices whose (i, j)th entry is f(X-i(T) X-j), where X-1,..., X-n are i.i.d. standa...
We consider random matrices whose entries are obtained by applying a (nonlinear) kernel function to ...
We consider n-by-n matrices whose (i, j)th entry is f(XTi Xj), where X1,..., Xn are i.i.d. standard ...
This paper reveals and examines the relationship between the solution and stability of Fredholm inte...
We study the spectra of p×p random matrices K with off-diagonal (i, j) entry equal to n−1/2k(XTi Xj/...
The spectral behavior of kernel matrices built from complex multi-variate data is established in the...
Kernel methods are an extremely popular set of techniques used for many important machine learning a...
In this dissertation, it is first shown that, when the radial basis function is a $p$-norm and $1 2...
We use the steepest descents method to study the integral kernel of a family of normal random matrix...
We use methods from the Fock space and Segal–Bargmann theories to prove several results on the Gauss...
AbstractInterpolation problems for analytic radial basis functions like the Gaussian and inverse mul...
We address the local spectral behavior of the random matrix Π_1U^(⊗k)Π_2U^(⊗k∗)Π_1 where U is ...