ASRL:A robust loss function with potential for development
This work presents an incremental improvement in robust loss functions for regression problems, potentially benefiting researchers and practitioners in machine learning.
The authors proposed a partition-wise robust loss function that achieves high robustness and wide applicability through adaptive parameter adjustment, and demonstrated its advantages by comparing it with other loss functions on five diverse datasets for regression tasks.
In this article, we proposed a partition:wise robust loss function based on the previous robust loss function. The characteristics of this loss function are that it achieves high robustness and a wide range of applicability through partition-wise design and adaptive parameter adjustment. Finally, the advantages and development potential of this loss function were verified by applying this loss function to the regression question and using five different datasets (with different dimensions, different sample numbers, and different fields) to compare with the other loss functions. The results of multiple experiments have proven the advantages of our loss function .