Design of multiresolution operators using statistical learning tools: application to compression of signals

Authors:
Francesc Aràndiga;Albert Cohen;Dionisio F. Yáñez
Affiliations:
Dept. Matemática Aplicada, Universitat de València, Burjassot, Valencia, Spain;Laboratoire Jacques-Louis Lions, UniversitéPierre et Marie Curie, Paris, France;Dept. Matemáticas, CC. Naturales y CC. Sociales aplicadas a la Educación, U. Católica de Valencia, Godella, Valencia, Spain
Venue:
Proceedings of the 7th international conference on Curves and Surfaces
Year:
2010

Citing 6
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Multiresolution representation of data: a general framework

SIAM Journal on Numerical Analysis
Multiresolution Based on Weighted Averages of the Hat Function II: Nonlinear Reconstruction Techniques

SIAM Journal on Scientific Computing
Convex Optimization

Convex Optimization
ENO Multiresolution Schemes with General Discretizations

SIAM Journal on Numerical Analysis
A fully adaptive multiresolution scheme for image processing

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Using multiresolution based on Harten's framework [J. Appl. Numer. Math., 12 (1993), pp. 153---192.] we introduce an alternative to construct a prediction operator using Learning statistical theory. This integrates two ideas: generalized wavelets and learning methods, and opens several possibilities in the compressed signal context. We obtain theoretical results which prove that this type of schemes (LMR schemes) are equal to or better than the classical schemes. Finally, we compare traditional methods with the algorithm that we present in this paper.