English

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}
}