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In this paper we continue to investigate a certain class of Hankel-like positive definite kernels using their associated orthogonal polynomials. The main result of this paper is about the structure of this kind of kernels.

Functional Analysis · Mathematics 2007-05-23 T. Banks , T. Constantinescu

We present new conditions for semigroups of positive operators to converge strongly as time tends to infinity. Our proofs are based on a novel approach combining the well-known splitting theorem by Jacobs, de Leeuw and Glicksberg with a…

Functional Analysis · Mathematics 2019-01-29 Moritz Gerlach , Jochen Glück

We consider kernels of discrete convolution operators or, equivalently, homogeneous solutions of partial difference operators and show that these solutions always have to be exponential polynomials. The respective polynomial space in…

Numerical Analysis · Mathematics 2014-04-01 Tomas Sauer

We investigate the interaction between the existence of reproducing kernels on infinite-dimensional Hermitian vector bundles and the positivity properties of the corresponding bundles. The positivity refers to the curvature form of certain…

Functional Analysis · Mathematics 2014-02-04 Daniel Beltita , José E. Galé

This is the first of a series of papers where we develop a theory of total positivity for loop groups. In this paper, we completely describe the totally nonnegative part of the polynomial loop group GL_n(\R[t,t^{-1}]), and for the formal…

Combinatorics · Mathematics 2009-12-06 Thomas Lam , Pavlo Pylyavskyy

We discuss a (i) quantized version of the Jordan decomposition theorem for a complex Borel measure on a compact Hausdorff space, namely, the more general problem of decomposing a general noncommutative kernel (a quantization of the standard…

Operator Algebras · Mathematics 2022-02-04 Joseph A. Ball , Gregory Marx , Victor Vinnikov

In this paper, we specify what functions induce the bounded composition operators on a reproducing kernel Hilbert space (RKHS) associated with an analytic positive definite function defined on $\mathbf{R}^d$. We prove that only affine…

Functional Analysis · Mathematics 2022-03-11 Masahiro Ikeda , Isao Ishikawa , Yoshihiro Sawano

Hilbertian kernel methods and their positive semidefinite kernels have been extensively used in various fields of applied mathematics and machine learning, owing to their several equivalent characterizations. We here unveil an analogy with…

Functional Analysis · Mathematics 2023-01-10 Pierre-Cyril Aubin-Frankowski , Stéphane Gaubert

The purpose of this article is threefold: Firstly, we propose some enhancements to the existing definition of 6-functor formalisms. Secondly, we systematically study the category of kernels, which is a certain 2-category attached to every…

Category Theory · Mathematics 2024-10-18 Claudius Heyer , Lucas Mann

We prove that the only entrywise transforms of rectangular matrices which preserve total positivity or total non-negativity are either constant or linear. This follows from an extended classification of preservers of these two properties…

Functional Analysis · Mathematics 2020-06-25 Alexander Belton , Dominique Guillot , Apoorva Khare , Mihai Putinar

In this paper we investigate the class of invariant positive definite kernels on the free semigroup on N generators. We provide a combinatorial description of the positivity of the kernel in terms of Dyck paths and then we find a…

Functional Analysis · Mathematics 2007-05-23 T. Banks , T. Constantinescu , Nermine El-Sissi

We present a characterization for a positive definite operator valued kernel to be universal or $C_{0}$-universal, and apply these characterizations to a family of operator valued kernels that are shown to be well behaved. Later, we obtain…

Functional Analysis · Mathematics 2020-03-26 Jean Carlo Guella

A rectangular matrix is called totally positive, if all its minors are positive. A point of a real Grassmanian manifold $G_{l,m}$ of $l$-dimensional subspaces in $\mathbb R^m$ is called strictly totally positive, if one can normalize its…

Dynamical Systems · Mathematics 2018-05-10 Victor Buchstaber , Alexey Glutsyuk

Given a fixed sigma-finite measure space $\left(X,\mathscr{B},\nu\right)$, we shall study an associated family of positive definite kernels $K$. Their factorizations will be studied with view to their role as covariance kernels of a variety…

Spectral Theory · Mathematics 2018-10-31 Palle Jorgensen , Feng Tian

We characterize all linear operators on finite or infinite-dimensional spaces of univariate real polynomials preserving the sets of elliptic, positive, and non-negative polynomials, respectively. This is done by means of Fischer-Fock…

Classical Analysis and ODEs · Mathematics 2009-02-04 Julius Borcea

With view to applications in stochastic analysis and geometry, we introduce a new correspondence for positive definite kernels (p.d.) $K$ and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of…

Functional Analysis · Mathematics 2019-11-28 Palle Jorgensen , Feng Tian

$C^*$-algebra-valued kernels could pave the way for the next generation of kernel machines. To further our fundamental understanding of learning with $C^*$-algebraic kernels, we propose a new class of positive definite kernels based on the…

Machine Learning · Statistics 2025-03-11 Yuka Hashimoto , Ayoub Hafid , Masahiro Ikeda , Hachem Kadri

The universality properties of kernels characterize the class of functions that can be approximated in the associated reproducing kernel Hilbert space and are of fundamental importance in the theoretical underpinning of kernel methods in…

Machine Learning · Computer Science 2025-06-25 Franziskus Steinert , Salem Said , Cyrus Mostajeran

In this paper, we illustrate the effectiveness of reproducing kernel Hilbert space techniques in the study of composition operators. For weighted Hardy spaces on the unit disk, we characterize the composition operators whose adjoint is…

Functional Analysis · Mathematics 2026-01-28 Preeti Kumari , P. Muthukumar , Antti Rasila

This paper reviews the functional aspects of statistical learning theory. The main point under consideration is the nature of the hypothesis set when no prior information is available but data. Within this framework we first discuss about…

Machine Learning · Statistics 2016-11-25 Stephane Canu , Xavier Mary , Alain Rakotomamonjy