Robust regression for mixed Poisson-Gaussian model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10385080" target="_blank" >RIV/00216208:11320/18:10385080 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s11075-017-0463-1" target="_blank" >https://doi.org/10.1007/s11075-017-0463-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11075-017-0463-1" target="_blank" >10.1007/s11075-017-0463-1</a>

Result language
angličtina
Original language name
Robust regression for mixed Poisson-Gaussian model
Original language description
This paper focuses on efficient computational approaches to compute approximate solutions of a linear inverse problem that is contaminated with mixed Poisson-Gaussian noise, and when there are additional outliers in the measured data. The Poisson-Gaussian noise leads to a weighted minimization problem, with solution-dependent weights. To address outliers, the standard least squares fit-to-data metric is replaced by the Talwar robust regression function. Convexity, regularization parameter selection schemes, and incorporation of non-negative constraints are investigated. A projected Newton algorithm is used to solve the resulting constrained optimization problem, and a preconditioner is proposed to accelerate conjugate gradient Hessian solves. Numerical experiments on problems from image deblurring illustrate the effectiveness of the methods.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics

Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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