Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics

Understand

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.