Fitting Generalized Multivariate Huber Loss Functions.

IEEE Signal Processing Letters（2016）

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摘要

In this letter, we consider a class of generalized multivariate Huber (GMH) loss functions. Our goal is parameter estimation in linear models contaminated by non-Gaussian noise. We assume access to a secondary dataset of independent noise realizations, and we use these data to fit a convex GMH function that will then lead to efficient parameter estimation. Our framework includes the classical weig...

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关键词

Parameter estimation,Computational modeling,Standards,Estimation,Gaussian distribution,Minimization,Signal processing algorithms

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