Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics
Machine Learning
2021-08-31 v1 Machine Learning
Abstract
We propose a generalized formulation of the Huber loss. We show that with a suitable function of choice, specifically the log-exp transform; we can achieve a loss function which combines the desirable properties of both the absolute and the quadratic loss. We provide an algorithm to find the minimizer of such loss functions and show that finding a centralizing metric is not that much harder than the traditional mean and median.
Cite
@article{arxiv.2108.12627,
title = {Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics},
author = {Kaan Gokcesu and Hakan Gokcesu},
journal= {arXiv preprint arXiv:2108.12627},
year = {2021}
}