Regularized Regression and Density Estimation based on Optimal Transport

Handle URI:
http://hdl.handle.net/10754/599489
Title:
Regularized Regression and Density Estimation based on Optimal Transport
Authors:
Burger, M.; Franek, M.; Schonlieb, C.-B.
Abstract:
The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).
Citation:
Burger M, Franek M, Schonlieb C-B (2012) Regularized Regression and Density Estimation based on Optimal Transport. Applied Mathematics Research eXpress. Available: http://dx.doi.org/10.1093/amrx/abs007.
Publisher:
Oxford University Press (OUP)
Journal:
Applied Mathematics Research eXpress
KAUST Grant Number:
KUK-I1-007-43
Issue Date:
11-Mar-2012
DOI:
10.1093/amrx/abs007
Type:
Article
ISSN:
1687-1200; 1687-1197
Sponsors:
The work of M.B. has been supported by the German Science Foundation (DFG) through project Regularization with Singular Energies. C.B.S acknowledges the financial support provided by the Cambridge Centre for Analysis (CCA), the DFG Graduiertenkolleg 1023 Identification in Mathematical Models: Synergy of Stochastic and Numerical Methods and the project WWTF Five senses-Call 2006, Mathematical Methods for Image Analysis and Processing in the Visual Arts. Further, this publication is based on work supported by Award No. KUK-I1-007-43, made by King Abdullah University of Science and Technology (KAUST).
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Full metadata record

DC FieldValue Language
dc.contributor.authorBurger, M.en
dc.contributor.authorFranek, M.en
dc.contributor.authorSchonlieb, C.-B.en
dc.date.accessioned2016-02-28T05:52:04Zen
dc.date.available2016-02-28T05:52:04Zen
dc.date.issued2012-03-11en
dc.identifier.citationBurger M, Franek M, Schonlieb C-B (2012) Regularized Regression and Density Estimation based on Optimal Transport. Applied Mathematics Research eXpress. Available: http://dx.doi.org/10.1093/amrx/abs007.en
dc.identifier.issn1687-1200en
dc.identifier.issn1687-1197en
dc.identifier.doi10.1093/amrx/abs007en
dc.identifier.urihttp://hdl.handle.net/10754/599489en
dc.description.abstractThe aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).en
dc.description.sponsorshipThe work of M.B. has been supported by the German Science Foundation (DFG) through project Regularization with Singular Energies. C.B.S acknowledges the financial support provided by the Cambridge Centre for Analysis (CCA), the DFG Graduiertenkolleg 1023 Identification in Mathematical Models: Synergy of Stochastic and Numerical Methods and the project WWTF Five senses-Call 2006, Mathematical Methods for Image Analysis and Processing in the Visual Arts. Further, this publication is based on work supported by Award No. KUK-I1-007-43, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherOxford University Press (OUP)en
dc.titleRegularized Regression and Density Estimation based on Optimal Transporten
dc.typeArticleen
dc.identifier.journalApplied Mathematics Research eXpressen
dc.contributor.institutionWestfalische Wilhelms-Universitat Munster, Munster, Germanyen
dc.contributor.institutionUniversity of Cambridge, Cambridge, United Kingdomen
kaust.grant.numberKUK-I1-007-43en
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