Publications associées à Mehler kernel

Fourier Sampling in Signal Processing and Numerical Linear Algebra

This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...

EPFL2020

Slepian Guided Filtering of Graph Signals

Dimitri Nestor Alice Van De Ville, Miljan Petrovic

Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Kantorovich distance in the martingale CLT and quantitative homogenization of parabolic equations with random coefficients

Jean-Christophe Mourrat

The article begins with a quantitative version of the martingale central limit theorem, in terms of the Kantorovich distance. This result is then used in the study of the homogenization of discrete parabolic equations with random i.i.d. coefficients. For s ...

Springer Heidelberg2014

A Probabilistic Approach of Sum Rules for Heat Polynomials

Olivier Lévêque

In this paper, we show that the sum rules for generalized Hermite polynomials derived by Daboul and Mizrahi (2005 J. Phys. A: Math. Gen. 38 427-48) and by Graczyk and Nowak (2004 C. R. Acad. Sci., Ser. 1 338 849) can be interpreted and easily recovered usi ...

2012

Sparse spectral approximations for computing polynomial functionals

Fabio Nobile

We give a new fast method for evaluating sprectral approximations of nonlinear polynomial functionals. We prove that the new algorithm is convergent if the functions considered are smooth enough, under a general assumption on the spectral eigenfunctions th ...

EPFL-SB-MATHICSE2012

Fast O(1) Bilateral Filtering Using Trigonometric Range Kernels

Michaël Unser, Daniel Sage, Kunal Narayan Chaudhury

It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithm ...

IEEE2011