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We are studying the problem of estimating density in a wide range of metric spaces, including the Euclidean space, the sphere, the ball, and various Riemannian manifolds. Our framework involves a metric space with a doubling measure and a…

Statistics Theory · Mathematics 2023-04-04 Galatia Cleanthous , Athanasios G. Georgiadis , Philip A. White

For a smooth immersion $f$ from the punctured disk $D\backslash\{0\}$ into $\mathbb{R}^n$ extendable continuously at the puncture, if its mean curvature is square integrable and the measure of $f(D)\cap B_{r_k}=o(r_k)$ for a sequence…

Differential Geometry · Mathematics 2015-09-29 Jingyi Chen , Yuxiang Li

In the paper we consider the polyharmonic Bergman space for the union of the rotated unit Euclidean balls. Using so called zonal polyharmonics we derive the formulas for the kernel of this space. Moreover, we study the weighted polyharmonic…

Complex Variables · Mathematics 2019-12-05 Hubert Grzebuła

Based on direct integrals, a framework allowing to integrate a parametrised family of reproducing kernels with respect to some measure on the parameter space is developed. By pointwise integration, one obtains again a reproducing kernel…

Functional Analysis · Mathematics 2012-02-21 Thomas Hotz , Fabian J. E. Telschow

Non-Hermitian random matrices with symplectic symmetry provide examples for Pfaffian point processes in the complex plane. These point processes are characterised by a matrix valued kernel of skew-orthogonal polynomials. We develop their…

Mathematical Physics · Physics 2022-01-19 Gernot Akemann , Markus Ebke , Iván Parra

We investigate the boundedness of oscillating singular integrals on Lie groups of polynomial growth in order to extend the classical oscillating conditions due to Fefferman and Stein for the boundedness of oscillating convolution operators.…

Differential Geometry · Mathematics 2022-07-15 Duván Cardona , Michael Ruzhansky

This article focuses on trigonometric Riemann B-splines and Riemann kernels of trigonometric interpolation splines of arbitrary order; it is shown that trigonometric interpolation splines are a convolution of trigonometric B-splines with…

Numerical Analysis · Mathematics 2023-10-31 Volodymyr Denysiuk , Olena Hryshko

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different…

Chemical Physics · Physics 2023-03-08 Filippo Bigi , Sergey N. Pozdnyakov , Michele Ceriotti

We introduce a theory of local kernels, which generalize the kernels used in the standard diffusion maps construction of nonparametric modeling. We prove that evaluating a local kernel on a data set gives a discrete representation of the…

Classical Analysis and ODEs · Mathematics 2015-01-07 Tyrus Berry , Timothy Sauer

Associated to the Bergman kernels of a polarized toric \kahler manifold $(M, \omega, L, h)$ are sequences of measures $\{\mu_k^z\}_{k=1}^{\infty}$ parametrized by the points $z \in M$. For each $z$ in the open orbit, we prove a central…

Probability · Mathematics 2022-06-14 Steve Zelditch , Peng Zhou

The development of a metric for structural data is a long-term problem in pattern recognition and machine learning. In this paper, we develop a general metric for comparing nonlinear dynamical systems that is defined with Perron-Frobenius…

Machine Learning · Statistics 2018-11-01 Isao Ishikawa , Keisuke Fujii , Masahiro Ikeda , Yuka Hashimoto , Yoshinobu Kawahara

We investigate the geometry of Hermitian manifolds endowed with a compact Lie group action by holomorphic isometries with principal orbits of codimension one. In particular, we focus on a special class of these manifolds constructed by…

Differential Geometry · Mathematics 2023-03-31 Daniele Angella , Francesco Pediconi

In this paper we show how ideas from spline theory can be used to construct a local basis for the space of translates of a general iterated Brownian Bridge kernel $k_{\beta,\varepsilon}$ for $\beta\in\mathbb{N}$, $\varepsilon\geq 0$. In the…

Numerical Analysis · Mathematics 2023-06-09 Gabriele Santin

The article introduces an elementary cost and storage reduction method for spectral clustering and principal component analysis. The method consists in randomly "puncturing" both the data matrix $X\in\mathbb{C}^{p\times n}$ (or…

Machine Learning · Computer Science 2021-05-18 Romain Couillet , Florent Chatelain , Nicolas Le Bihan

We obtain new explicit formulas for the Bergman kernel function on two families of Hartogs domains. To do so, we first compute the Bergman kernels on the slices of these Hartogs domains with some coordinates fixed, evaluate these kernel…

Complex Variables · Mathematics 2015-12-03 Zhenghui Huo

We give a precise estimate of the Bergman kernel for the model domain defined by $\Omega_F=\{(z,w)\in \mathbb{C}^{n+1}:{\rm Im}w-|F(z)|^2>0\},$ where $F=(f_1,...,f_m)$ is a holomorphic map from $\mathbb{C}^n$ to $\mathbb{C}^m$, in terms of…

Complex Variables · Mathematics 2015-05-14 Bo-Yong Chen , Hanjin Lee

We study the concentration of random kernel matrices around their mean. We derive nonasymptotic exponential concentration inequalities for Lipschitz kernels assuming that the data points are independent draws from a class of multivariate…

Statistics Theory · Mathematics 2020-01-28 Arash A. Amini , Zahra S. Razaee

In this paper we study the Bergman kernel and projection on the unbounded worm domain $$ \mathcal{W}_\infty = \big\{(z_1,z_2)\in\mathbb{C}^2 : \big|z_1-e^{i\log|z_2|^2}\big|^2<1, z_2\neq0\big\}. $$ We first show that the Bergman space of…

Complex Variables · Mathematics 2020-09-08 Steven G. Krantz , Marco M. Peloso , Caterina Stoppato

In this paper, weighted Bergman spaces on the unit ball in C^n are investigated. A characterization of the Carleson embeddings is established. Pointwise and norm estimates on the reproducing kernel function of weighted Bergman spaces on the…

Complex Variables · Mathematics 2026-05-26 Nihat Gökhan Göğüş , Sinem Yelda Sönmez

Random feature maps are used to decrease the computational cost of kernel machines in large-scale problems. The Mondrian kernel is one such example of a fast random feature approximation of the Laplace kernel, generated by a computationally…

Machine Learning · Computer Science 2025-03-13 Calvin Osborne , Eliza O'Reilly
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