Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics

2022/02/22 by Kaan Gökcesu, Gokcesu, Kaan, Hakan Gökcesu +1 · 1 citation
Engineering · #Computation (stat.CO) #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.2202.11141

openalex publication_date 2022/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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