Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics
Machine Learning
2022-02-24 v1 Machine Learning
Computation
Abstract
We propose an extended generalization of the pseudo Huber loss formulation. We show that using the log-exp transform together with the logistic function, we can create a loss which combines the desirable properties of the strictly convex losses with robust loss functions. With this formulation, we show that a linear convergence algorithm can be utilized to find a minimizer. We further discuss the creation of a quasi-convex composite loss and provide a derivative-free exponential convergence rate algorithm.
Keywords
Cite
@article{arxiv.2202.11141,
title = {Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics},
author = {Kaan Gokcesu and Hakan Gokcesu},
journal= {arXiv preprint arXiv:2202.11141},
year = {2022}
}