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Related papers: One-sided Cauchy-Stieltjes Kernel Families

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We introduce a novel family of projected distributions on the circle and the sphere, namely the circular and spherical projected Cauchy distributions, as promising alternatives for modelling circular and spherical data. The circular…

Methodology · Statistics 2024-09-12 Michail Tsagris , Omar Alzeley

Two ubiquitous aspects of large-scale data analysis are that the data often have heavy-tailed properties and that diffusion-based or spectral-based methods are often used to identify and extract structure of interest. Perhaps surprisingly,…

Machine Learning · Computer Science 2010-05-11 Michael W. Mahoney , Hariharan Narayanan

We study equivalence of invariant metrics on noncompact K\"ahler manifolds with a complete Bergman metric of bounded curvature. Especially only the boundedness of the ratio between Bergman kernel and the $n$-times wedge product of Bergman…

Differential Geometry · Mathematics 2023-12-04 Gunhee Cho , Kyu-Hwan Lee

Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selected based on characteristics of the problem and the data. This kernel is then employed to…

Machine Learning · Statistics 2024-03-12 Paul Dommel , Alois Pichler

Practical applications of kernel methods often use variable bandwidth kernels, also known as self-tuning kernels, however much of the current theory of kernel based techniques is only applicable to fixed bandwidth kernels. In this paper, we…

Spectral Theory · Mathematics 2015-01-15 Tyrus Berry , John Harlim

We show that, given a family of discs centered at a chord-arc curve, the analytic capacity of a union of arbitrary subsets of these discs (one subset in each disc) is comparable with the sum of their analytic capacities. We show a sort of…

Analysis of PDEs · Mathematics 2014-04-09 Vladimir Eiderman , Alexander Reznikov , Alexander Volberg

Various kinds of Stieltjes integrals using gauge integration have become highly popular in the field of differential equations and other applications. In the theories of integration and of ordinary differential equations, convergence…

Classical Analysis and ODEs · Mathematics 2022-11-15 Umi Mahnuna Hanung

We define a measure on tuples of partitions, called the symplectic Schur process, that should be regarded as the right analogue of the Schur process of Okounkov-Reshetikhin for the Cartan type C. The weights of our measure include factors…

Mathematical Physics · Physics 2026-04-30 Cesar Cuenca , Matteo Mucciconi

We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e.g., error bounds) may be available…

Machine Learning · Computer Science 2020-03-19 Yongxin Chen , Tryphon T. Georgiou , Allen R. Tannenbaum

We propose a definition of a "$C^*$-Eberlein" algebra, which is a weak form of a $C^*$-bialgebra with a sort of "unitary generator". Our definition is motivated to ensure that commutative examples arise exactly from semigroups of…

Functional Analysis · Mathematics 2021-09-15 Biswarup Das , Matthew Daws

For a class of one-dimensional determinantal point processes including those induced by orthogonal projections with integrable kernels satisfying a growth condition, it is proved that their conditional measures, with respect to the…

Probability · Mathematics 2016-05-05 Alexander I. Bufetov

We study representations of positive definite kernels $K$ in a general setting, but with view to applications to harmonic analysis, to metric geometry, and to realizations of certain stochastic processes. Our initial results are stated for…

Functional Analysis · Mathematics 2017-06-30 Palle Jorgensen , Feng Tian

The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered. Kernels and their associated Hilbert spaces are the main formalism employed herein along with an observational model where…

Machine Learning · Computer Science 2022-09-13 Paul Scharnhorst , Emilio T. Maddalena , Yuning Jiang , Colin N. Jones

We present an operator-free, measure-theoretic approach to the conditional mean embedding (CME) as a random variable taking values in a reproducing kernel Hilbert space. While the kernel mean embedding of unconditional distributions has…

Machine Learning · Computer Science 2021-01-11 Junhyung Park , Krikamol Muandet

The Lipschitz geometry of segments of the infinite Hamming cube is studied. Tight estimates on the distortion necessary to embed the segments into spaces of continuous functions on countable compact metric spaces are given. As an…

Functional Analysis · Mathematics 2017-09-27 F. Baudier , D. Freeman , Th. Schlumprecht , A. Zsák

We study infinite products of reproducing kernels with view to their use in dynamics (of iterated function systems), in harmonic analysis, and in stochastic processes. On the way, we construct a new family of representations of the Cuntz…

Functional Analysis · Mathematics 2013-01-22 D. Alpay , P. Jorgensen , I. Lewkowicz , I. Martziano

Let G be a split real Kac-Moody group of arbitrary type and let K be its maximal compact subgroup, i.e. the subgroup of elements fixed by a Cartan-Chevalley involution of G. We construct non-trivial spin covers of K, thus confirming a…

Group Theory · Mathematics 2015-02-26 David Ghatei , Max Horn , Ralf Köhl , Sebastian Weiß

We construct an invariant of closed ${\rm spin}^c$ 4-manifolds using families of Seiberg-Witten equations. This invariant is formulated as a cohomology class on a certain abstract simplicial complex consisting of embedded surfaces of a…

Geometric Topology · Mathematics 2021-11-05 Hokuto Konno

This paper gives a first step toward extending the theory of Fourier-Stieltjes algebras from groups to groupoids. If G is a locally compact (second countable) groupoid, we show that B(G), the linear span of the Borel positive definite…

Operator Algebras · Mathematics 2016-09-06 Arlan Ramsay , Martin E. Walter

Kernel-based testing has revolutionized the field of non-parametric tests through the embedding of distributions in an RKHS. This strategy has proven to be powerful and flexible, yet its applicability has been limited to the standard…

Methodology · Statistics 2024-11-28 Anthony Ozier-Lafontaine , Polina Arsenteva , Franck Picard , Bertrand Michel
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